Skip to content

FoundationModel

skforecast.foundation._foundation_model.FoundationModel 

FoundationModel(model_id, **kwargs)

Scikit-learn compatible interface for foundation time-series models.

Currently supports Amazon Chronos-2, Google TimesFM 2.5, Salesforce Moirai-2, TabICLv2, TabPFN-TS, TFC-T0, Synthefy Nori and EDF Lab TS-ICL. For full skforecast ecosystem integration (backtesting, model selection, etc.) use ForecasterFoundation instead.

Parameters:

Name Type Description Default
model_id str

HuggingFace model ID. The adapter is resolved automatically from the model_id prefix. Available model IDs:

Amazon Chronos-2 (supports exog):

  • 'amazon/chronos-2'
  • 'autogluon/chronos-2-small'
  • 'autogluon/chronos-2-synth'

Google TimesFM 2.5 (does not support exog):

  • 'google/timesfm-2.5-200m-pytorch'

Salesforce Moirai-2 (does not support exog):

  • 'Salesforce/moirai-2.0-R-small'

TabICLv2 (supports exog):

  • 'soda-inria/tabicl'

Prior Labs TabPFN-TS (supports exog):

  • 'priorlabs/tabpfn-ts'

The Forecasting Company T0 (supports exog):

  • 'theforecastingcompany/t0-alpha'

Synthefy Nori (supports exog):

  • 'Synthefy/Nori'

EDF Lab TS-ICL (supports exog):

  • 'taharnbl/TS-ICL'

See References for links to model documentation and model cards.

 required
**kwargs Any

Additional keyword arguments forwarded to the underlying adapter. Valid keys depend on the model selected by model_id. See the corresponding adapter class documentation or the model card linked in the References section below for the full parameter list.

Commonly used kwargs by model:

  • Amazon Chronos-2 (ChronosAdapter): context_length (int, default 8192), device_map (str, default 'auto'), torch_dtype (object, default None), predict_kwargs (dict, default None), cross_learning (bool, default False).
  • Google TimesFM 2.5 (TimesFMAdapter): context_length (int, default 512), max_horizon (int, default 512), forecast_config_kwargs (dict, default None).
  • Salesforce Moirai-2 (MoiraiAdapter): context_length (int, default 2048), device (str, default 'auto').
  • TabICLv2 (TabICLAdapter): context_length (int, default 4096), point_estimate (str, default 'mean'), tabicl_config (dict, default None), temporal_features (list, default None), show_progress (bool, default False; set to True to show the tqdm bar emitted during inference).
  • Prior Labs TabPFN-TS (TabPFNAdapter): context_length (int, default 32768), mode (str, default 'local'), point_estimate (str, default 'median'), tabpfn_model_config (dict, default None), temporal_features (list, default None), show_progress (bool, default False; set to True to show the tqdm bar emitted during inference).
  • The Forecasting Company T0 (T0Adapter): context_length (int, default 8192), device_map (str, default 'auto'), torch_dtype (object, default None).
  • Synthefy Nori (NoriAdapter): context_length (int, default 4096), point_estimate (str, default 'mean'), add_calendar_features (bool, default True), n_fourier_terms (int, default 2), nori_config (dict, default None).
  • EDF Lab TS-ICL (TSICLAdapter): checkpoint_version (str, default 'tsicl-v1.ckpt'), context_length (int, default 4096), device (str, default 'auto'), allow_auto_download (bool, default True).
 {}

Attributes:

Name Type Description
adapter object

The underlying adapter instance, instantiated automatically based on the model_id prefix. The concrete type depends on the model — e.g. ChronosAdapter for autogluon/chronos-* models.
model_id str

HuggingFace model ID. Mirrors adapter.model_id.
context_ dict[str, pandas Series]

Per-series dict of pandas Series containing the last context_length observations from the training data, stored during fit. Mirrors adapter.context_.
context_exog_ dict

Per-series dict of pandas DataFrame containing the last context_length exog variables from the training data, stored during fit. None if the adapter does not support exogenous variables or no exog was provided. Mirrors adapter.context_exog_.
context_length int

Maximum number of historical observations used as context. Mirrors adapter.context_length.
allow_exog bool

Whether the underlying adapter supports exogenous variables.
index_type_ type

Type of index of the input used in training.
index_freq_ pandas DateOffset, int

Frequency of the index of the input used in training. A pandas DateOffset for DatetimeIndex or an int step for RangeIndex.
context_range_ dict[str, pandas Index]

First and last values of the index of the data used during training for each series.
series_names_in_ list

Names of the series (levels) provided by the user during training.
is_multiple_series_ bool

Whether the model was fitted with multiple series.
exog_in_ bool

If the model has been trained using exogenous variable/s.
exog_names_in_ list

Names of the exogenous variables used during training. None if no exog was provided.
exog_names_in_per_series_ dict

Names of the exogenous variables used during training for each series. None if no exog was provided.
exog_type_in_ type

Type of exogenous variable/s used in training. None if no exog was provided.
creation_date str

Date of creation.
is_fitted bool

Tag to identify if the model has been fitted (trained).
fit_date str

Date of last fit.
skforecast_version str

Version of skforecast library used to create the model.
python_version str

Version of python used to create the model.
Notes

Each adapter imports its own backend library lazily (i.e. inside the method that first needs it) rather than at module level. This means that only the library required by the adapter you actually use needs to be installed, other foundation-model backends remain optional.

Device handling is not uniform across adapters; each mirrors the convention of its own backend, so the kwarg used to select the device differs by model:

  • device_map (plus torch_dtype), HuggingFace from_pretrained style: Chronos, T0.
  • device string, resolved internally to a concrete accelerator: Moirai, TS-ICL.
  • Through the backend configuration dict: TabICL (tabicl_config), TabPFN (tabpfn_model_config), Nori (nori_config).
  • No device parameter: TimesFM, which relies on its backend's own default device selection.
References

.. [1] Amazon Chronos - GitHub repository. https://github.com/amazon-science/chronos-forecasting

.. [2] Amazon Chronos - HuggingFace collection. https://huggingface.co/collections/amazon/chronos-models-65f1791d630a8d57cb718444

.. [3] Google TimesFM - GitHub repository. https://github.com/google-research/timesfm

.. [4] Google TimesFM - HuggingFace collection. https://huggingface.co/collections/google/timesfm-release-66e4be5fdb56e960c1e482a6

.. [5] Salesforce Moirai (uni2ts) - GitHub repository. https://github.com/SalesforceAIResearch/uni2ts

.. [6] Salesforce Moirai-R - HuggingFace collection. https://huggingface.co/collections/Salesforce/moirai-r-models-65c8d3a94c51428c300e0742

.. [7] TabICL - GitHub repository. https://github.com/soda-inria/tabicl

.. [8] TabICL - Documentation. https://tabicl.readthedocs.io/en/latest/

.. [9] TabPFN-TS - GitHub repository. https://github.com/PriorLabs/tabpfn-time-series

.. [10] Prior Labs TabPFN - Documentation. https://docs.priorlabs.ai/

.. [11] The Forecasting Company T0 - GitHub repository. https://github.com/theforecastingcompany/tfc-t0

.. [12] The Forecasting Company T0 - HuggingFace model card. https://huggingface.co/theforecastingcompany/t0-alpha

.. [13] Synthefy Nori - GitHub repository. https://github.com/Synthefy/synthefy-nori

.. [14] Synthefy Nori - HuggingFace model card. https://huggingface.co/Synthefy/Nori

.. [15] EDF Lab TS-ICL - GitHub repository. https://github.com/EDF-Lab/ts-icl

.. [16] EDF Lab TS-ICL - HuggingFace model card. https://huggingface.co/taharnbl/TS-ICL

Methods:

Name Description
fit

Fit the model by storing the training series and optional exog.
predict

Predict n steps ahead.
get_params

Get parameters for this estimator (sklearn-compatible).
set_params

Set parameters for this estimator (sklearn-compatible).
Source code in skforecast/foundation/_foundation_model.py 
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
def __init__(
    self,
    model_id: str,
    **kwargs: Any,
) -> None:

    adapter_cls                    = _resolve_adapter(model_id)
    self.adapter                   = adapter_cls(model_id=model_id, **kwargs)
    self.index_type_               = None
    self.index_freq_               = None
    self.context_range_            = None
    self.series_names_in_          = None
    self.is_multiple_series_       = False
    self.exog_in_                  = False
    self.exog_names_in_            = None
    self.exog_names_in_per_series_ = None
    self.exog_type_in_             = None
    self.creation_date             = pd.Timestamp.today().strftime('%Y-%m-%d %H:%M:%S')
    self.fit_date                  = None
    self.skforecast_version        = __version__
    self.python_version            = sys.version.split(" ")[0]

Attributes

adapter instance-attribute 

adapter = adapter_cls(model_id=model_id, **kwargs)

index_type_ instance-attribute 

index_type_ = None

index_freq_ instance-attribute 

index_freq_ = None

context_range_ instance-attribute 

context_range_ = None

series_names_in_ instance-attribute 

series_names_in_ = None

is_multiple_series_ instance-attribute 

is_multiple_series_ = False

exog_in_ instance-attribute 

exog_in_ = False

exog_names_in_ instance-attribute 

exog_names_in_ = None

exog_names_in_per_series_ instance-attribute 

exog_names_in_per_series_ = None

exog_type_in_ instance-attribute 

exog_type_in_ = None

creation_date instance-attribute 

creation_date = pd.Timestamp.today().strftime(
    "%Y-%m-%d %H:%M:%S"
)

fit_date instance-attribute 

fit_date = None

skforecast_version instance-attribute 

skforecast_version = __version__

python_version instance-attribute 

python_version = sys.version.split(' ')[0]

model_id property 

model_id

HuggingFace model ID.

Returns:

Name Type Description
model_id str

HuggingFace model ID. Mirrors adapter.model_id.

context_ property 

context_

Context stored during fit, used as default context for predict if no override is provided.

Returns:

Name Type Description
context_ dict[str, Series]

Per-series dict of pandas Series containing the last context_length observations from the training data, stored during fit. Mirrors adapter.context_.

context_exog_ property 

context_exog_

Context stored during fit, used as default context for predict if no override is provided.

Returns:

Name Type Description
context_exog_ (dict[str, DataFrame], None)

Per-series dict of pandas DataFrame containing the last context_length exog variables from the training data, stored during fit. None if the adapter does not support exogenous variables or no exog was provided. Mirrors adapter.context_exog_.

context_length property 

context_length

Maximum number of historical observations used as context.

Returns:

Name Type Description
context_length int

Maximum context length. Mirrors adapter.context_length.

allow_exog property 

allow_exog

Whether the underlying adapter supports exogenous variables.

Returns:

Name Type Description
allow_exog bool

True if the adapter accepts and uses exog; False if it ignores covariates (e.g. TimesFM 2.5, Moirai-2).

is_fitted property 

is_fitted

Whether the model has been fitted.

Returns:

Name Type Description
is_fitted bool

True after fit has been called at least once, False otherwise.

Methods:

_repr_html_ 

_repr_html_()

HTML representation of the object. The "General Information" section is expanded by default.

Source code in skforecast/foundation/_foundation_model.py 
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
def _repr_html_(self) -> str:
    """
    HTML representation of the object.
    The "General Information" section is expanded by default.
    """

    style, unique_id = get_style_repr_html(self.is_fitted)

    params_html = "".join(
        f"<li><strong>{html.escape(str(k))}:</strong> {html.escape(str(v))}</li>"
        for k, v in self.adapter.get_params().items()
        if k != "model_id"
    )

    content = f"""
    <div class="container-{unique_id}">
        <p style="font-size: 1.5em; font-weight: bold; margin-block-start: 0.83em; margin-block-end: 0.83em;">{type(self).__name__}</p>
        <details open>
            <summary>General Information</summary>
            <ul>
                <li><strong>Model ID:</strong> {self.model_id}</li>
                <li><strong>Context length:</strong> {self.context_length}</li>
                <li><strong>Creation date:</strong> {self.creation_date}</li>
                <li><strong>Last fit date:</strong> {self.fit_date}</li>
                <li><strong>Skforecast version:</strong> {self.skforecast_version}</li>
                <li><strong>Python version:</strong> {self.python_version}</li>
            </ul>
        </details>
        <details>
            <summary>Model Parameters</summary>
            <ul>
                {params_html}
            </ul>
        </details>
        <p>
            <a href="https://skforecast.org/{__version__}/api/foundationmodel.html">&#128214; <strong>API Reference</strong></a>
            &nbsp;&nbsp;
            <a href="https://skforecast.org/{__version__}/user_guides/foundation-forecasting-models.html">&#128221; <strong>User Guide</strong></a>
        </p>
    </div>
    """

    return style + content

_check_preprocess_context 

_check_preprocess_context(series, exog=None)

Normalize and validate context input to a per-series dict.

Parameters:

Name Type Description Default
series pandas Series, pandas DataFrame, dict

Time series to normalize and validate.

  • If pandas Series: single-series mode.
  • If wide pandas DataFrame or dict[str, pandas Series]: multi-series mode.
 required
exog pandas Series, pandas DataFrame, dict

Exogenous variables aligned to series.

  • If pandas Series or pandas DataFrame: broadcast to all series.
  • If dict: per-series exogenous variables.
 None

Returns:

Name Type Description
context dict

Per-series dict of pandas Series, trimmed to the last context_length observations.
series_indexes dict

Index of each series before trimming.
series_names_in_ list

Names of the series.
context_exog dict or None

Per-series dict of exogenous DataFrames trimmed to the last context_length observations. None if exog is None.
exog_names_in_ list or None

Names of the exogenous variables. None if exog is None.
Source code in skforecast/foundation/_foundation_model.py 
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
def _check_preprocess_context(
    self,
    series: pd.Series | pd.DataFrame | dict[str, pd.Series],
    exog: (
        pd.Series
        | pd.DataFrame
        | dict[str, pd.DataFrame | pd.Series | None]
        | None
    ) = None,
) -> tuple[dict[str, pd.Series], dict[str, pd.Index], list[str], dict[str, pd.DataFrame | None] | None, list[str] | None]:
    """
    Normalize and validate context input to a per-series dict.

    Parameters
    ----------
    series : pandas Series, pandas DataFrame, dict
        Time series to normalize and validate.

        - If `pandas Series`: single-series mode.
        - If wide `pandas DataFrame` or `dict[str, pandas Series]`:
        multi-series mode.
    exog : pandas Series, pandas DataFrame, dict, default None
        Exogenous variables aligned to `series`.

        - If `pandas Series` or `pandas DataFrame`: broadcast to all
        series.
        - If `dict`: per-series exogenous variables.

    Returns
    -------
    context : dict
        Per-series dict of pandas Series, trimmed to the last
        `context_length` observations.
    series_indexes : dict
        Index of each series before trimming.
    series_names_in_ : list
        Names of the series.
    context_exog : dict or None
        Per-series dict of exogenous DataFrames trimmed to the last
        `context_length` observations. `None` if `exog` is `None`.
    exog_names_in_ : list or None
        Names of the exogenous variables. `None` if `exog` is `None`.

    """

    if len(series) == 0:
        raise ValueError("`series` cannot be an empty dictionary or an empty DataFrame.")

    series_dict, series_indexes = check_preprocess_series_foundation(series)
    series_names_in_ = list(series_dict.keys())

    if exog is not None:
        exog_dict, exog_names_in_ = check_preprocess_exog_multiseries(
            series_names_in_  = series_names_in_,
            series_index_type = type(series_indexes[series_names_in_[0]]),
            exog              = exog,
            exog_dict         = {name: None for name in series_names_in_},
        )

        # NOTE: As no trim is applied to the series, it is only needed to 
        # align exog.
        series_dict, exog_dict = align_series_and_exog_multiseries(
                                     series_dict      = series_dict,
                                     exog_dict        = exog_dict,
                                     trim_series_nan  = False,
                                 )

    context = {
        name: s.iloc[-self.context_length :]
        for name, s in series_dict.items()
    }
    if exog is not None:
        context_exog = {
            name: (
                e.iloc[-self.context_length :]
                if e is not None
                else None
            )
            for name, e in exog_dict.items()
        }
    else:
        context_exog = None
        exog_names_in_ = None

    return context, series_indexes, series_names_in_, context_exog, exog_names_in_

fit 

fit(series, exog=None)

Fit the model by storing the training series and optional exog.

Parameters:

Name Type Description Default
series pandas Series, pandas DataFrame, dict

Training time series.

  • If pandas Series: single-series mode.
  • If wide pandas DataFrame (each column = one series): multi-series mode.
  • If dict[str, pandas Series]: multi-series mode; keys are series names.
 required
exog pandas Series, pandas DataFrame, dict

Historical exogenous variables aligned to series.

  • If pandas Series or pandas DataFrame: broadcast to all series.
  • If dict: per-series exogenous variables.
 None

Returns:

Name Type Description
self FoundationModel
Source code in skforecast/foundation/_foundation_model.py 
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
def fit(
    self,
    series: pd.Series | pd.DataFrame | dict[str, pd.Series],
    exog: (
        pd.Series
        | pd.DataFrame
        | dict[str, pd.DataFrame | pd.Series | None]
        | None
    ) = None,
) -> FoundationModel:
    """
    Fit the model by storing the training series and optional exog.

    Parameters
    ----------
    series : pandas Series, pandas DataFrame, dict
        Training time series.

        - If `pandas Series`: single-series mode.
        - If wide `pandas DataFrame` (each column = one series):
        multi-series mode.
        - If `dict[str, pandas Series]`: multi-series mode; keys are
        series names.
    exog : pandas Series, pandas DataFrame, dict, default None
        Historical exogenous variables aligned to `series`.

        - If `pandas Series` or `pandas DataFrame`: broadcast to all
        series.
        - If `dict`: per-series exogenous variables.

    Returns
    -------
    self : FoundationModel

    """

    self.index_type_                = None
    self.index_freq_                = None
    self.context_range_             = None
    self.series_names_in_           = None
    self.is_multiple_series_        = False
    self.exog_in_                   = False
    self.exog_names_in_             = None
    self.exog_names_in_per_series_  = None
    self.exog_type_in_              = None
    self.fit_date                   = None

    context, series_indexes, series_names_in_, context_exog, exog_names_in_ = (
        self._check_preprocess_context(
            series = series,
            exog   = exog,
        )
    )

    self.adapter.fit(
        context      = context,
        context_exog = context_exog,
    )

    self.series_names_in_    = series_names_in_
    self.is_multiple_series_ = len(series_names_in_) > 1

    if context_exog is not None and len(exog_names_in_) > 0:
        self.exog_in_ = True
        self.exog_names_in_ = exog_names_in_
        self.exog_names_in_per_series_ = {
            k: list(v.columns) if v is not None else None
            for k, v in context_exog.items()
        }
        self.exog_type_in_ = type(exog)

    self.fit_date = pd.Timestamp.today().strftime('%Y-%m-%d %H:%M:%S')
    self.context_range_ = {k: v[[0, -1]] for k, v in series_indexes.items()}
    self.index_type_ = type(series_indexes[series_names_in_[0]])
    if isinstance(series_indexes[series_names_in_[0]], pd.DatetimeIndex):
        self.index_freq_ = series_indexes[series_names_in_[0]].freq
    else:
        self.index_freq_ = series_indexes[series_names_in_[0]].step

    return self

_exog_to_dict staticmethod 

_exog_to_dict(exog, series_names_in)

Normalize any supported exog format into a per-series dict.

Parameters:

Name Type Description Default
exog pandas Series, pandas DataFrame, dict

Future exogenous variables in any supported format.

  • If pandas Series (flat index): broadcast to all series.
  • If pandas Series (MultiIndex): converted to dict, then keyed per series.
  • If pandas DataFrame (flat index): broadcast to all series.
  • If pandas DataFrame (MultiIndex / long-format): converted to dict per series ID.
  • If dict: used directly, missing series keys filled as None.
 required
series_names_in list[str]

Series names that define the output dict keys.

 required

Returns:

Name Type Description
exog_dict dict

Per-series dict with exactly the keys in series_names_in.
Source code in skforecast/foundation/_foundation_model.py 
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
@staticmethod
def _exog_to_dict(
    exog: pd.Series | pd.DataFrame | dict[str, pd.DataFrame | pd.Series | None],
    series_names_in: list[str],
) -> dict[str, pd.DataFrame | pd.Series | None]:
    """
    Normalize any supported exog format into a per-series dict.

    Parameters
    ----------
    exog : pandas Series, pandas DataFrame, dict
        Future exogenous variables in any supported format.

        - If `pandas Series` (flat index): broadcast to all series.
        - If `pandas Series` (MultiIndex): converted to dict, then
          keyed per series.
        - If `pandas DataFrame` (flat index): broadcast to all series.
        - If `pandas DataFrame` (MultiIndex / long-format): converted
          to dict per series ID.
        - If `dict`: used directly, missing series keys filled as
          `None`.
    series_names_in : list[str]
        Series names that define the output dict keys.

    Returns
    -------
    exog_dict : dict
        Per-series dict with exactly the keys in `series_names_in`.

    """

    if isinstance(exog, dict):
        return {name: exog.get(name, None) for name in series_names_in}

    if isinstance(exog, pd.Series):
        if isinstance(exog.index, pd.MultiIndex):
            exog = exog.to_frame()
        else:
            return {name: exog for name in series_names_in}

    # At this point exog is always a DataFrame (original or coerced)
    if isinstance(exog.index, pd.MultiIndex):
        if not isinstance(exog.index.levels[1], pd.DatetimeIndex):
            raise TypeError(
                "The second level of the MultiIndex in `exog` must be a "
                "pandas DatetimeIndex. "
                f"Found {type(exog.index.levels[1])}."
            )
        per_series = {
            sid: group.droplevel(0)
            for sid, group in exog.groupby(level=0, sort=False)
        }
        warnings.warn(
            "Passing a long-format DataFrame as `exog` requires "
            "additional internal transformations, which can increase "
            "computational time. It is recommended to use a dictionary "
            "of pandas Series or DataFrames instead.",
            InputTypeWarning,
            stacklevel=5,
        )
        return {name: per_series.get(name, None) for name in series_names_in}

    return {name: exog for name in series_names_in}

_prepare_future_exog 

_prepare_future_exog(steps, context, exog, series_names_in)

Normalize, broadcast, and align future exogenous variables to the forecast horizon in a single pass.

Performs the full pipeline for future exog:

  1. Type coercion: long-format MultiIndex Series/DataFrame is converted to a dict keyed by series ID.
  2. Broadcast / dict normalisation: flat Series or DataFrame is broadcast to every series; a dict is filled with None for missing keys; None input produces an all-None dict.
  3. Temporal alignment: each per-series exog is aligned to the forecast horizon using the resolved context. For DatetimeIndex data, exog is reindexed to the exact expected range (NaN-filling gaps). For other index types a length check and optional RangeIndex start verification are applied.

This function is self-contained — it does not depend on any metadata stored at fit time. Alignment is driven entirely by the context that will be used for prediction.

Parameters:

Name Type Description Default
steps int

Number of steps ahead to forecast.

 required
context dict[str, pandas Series]

Per-series resolved context. Each value is a pandas Series whose index provides the reference end-point and frequency for alignment.

 required
exog pandas Series, pandas DataFrame, dict

Future exogenous variables in any supported format.

  • If None: returns {name: None ...} for every series.
  • If pandas Series (flat index): broadcast to all series.
  • If pandas Series (MultiIndex): converted to dict, then keyed per series.
  • If pandas DataFrame (flat index): broadcast to all series.
  • If pandas DataFrame (MultiIndex / long-format): converted to dict per series ID.
  • If dict: used directly, missing series keys filled as None.
 None
series_names_in list[str]

Series names that define the output dict keys.

 required

Returns:

Name Type Description
exog_aligned dict

Per-series dict with exactly the keys in series_names_in. Each non-None value is a pandas DataFrame with exactly steps rows aligned to the forecast horizon. Series inputs are coerced to single-column DataFrames.

Raises:

Type Description
TypeError

If exog is a long-format DataFrame whose second MultiIndex level is not a DatetimeIndex, or if exog is an unsupported type.
ValueError

If a non-DatetimeIndex exog has fewer than steps rows, or if a RangeIndex exog does not start at the expected position.
Source code in skforecast/foundation/_foundation_model.py 
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
def _prepare_future_exog(
    self,
    steps: int,
    context: dict[str, pd.Series],
    exog: (
        pd.Series
        | pd.DataFrame
        | dict[str, pd.DataFrame | pd.Series | None]
        | None
    ),
    series_names_in: list[str],
) -> dict[str, pd.DataFrame | None]:
    """
    Normalize, broadcast, and align future exogenous variables to the
    forecast horizon in a single pass.

    Performs the full pipeline for future exog:

    1. **Type coercion**: long-format MultiIndex Series/DataFrame is
    converted to a dict keyed by series ID.
    2. **Broadcast / dict normalisation**: flat Series or DataFrame is
    broadcast to every series; a dict is filled with `None` for
    missing keys; `None` input produces an all-None dict.
    3. **Temporal alignment**: each per-series exog is aligned to the
    forecast horizon using the resolved context. For `DatetimeIndex`
    data, exog is reindexed to the exact expected range (NaN-filling
    gaps). For other index types a length check and optional
    `RangeIndex` start verification are applied.

    This function is self-contained — it does not depend on any
    metadata stored at `fit` time. Alignment is driven entirely by the
    context that will be used for prediction.

    Parameters
    ----------
    steps : int
        Number of steps ahead to forecast.
    context : dict[str, pandas Series]
        Per-series resolved context. Each value is a pandas Series whose
        index provides the reference end-point and frequency for
        alignment.
    exog : pandas Series, pandas DataFrame, dict, default None
        Future exogenous variables in any supported format.

        - If `None`: returns `{name: None ...}` for every series.
        - If `pandas Series` (flat index): broadcast to all series.
        - If `pandas Series` (MultiIndex): converted to dict, then
        keyed per series.
        - If `pandas DataFrame` (flat index): broadcast to all series.
        - If `pandas DataFrame` (MultiIndex / long-format): converted
        to dict per series ID.
        - If `dict`: used directly, missing series keys filled as
        `None`.
    series_names_in : list[str]
        Series names that define the output dict keys.

    Returns
    -------
    exog_aligned : dict
        Per-series dict with exactly the keys in `series_names_in`. Each
        non-None value is a pandas DataFrame with exactly `steps` rows
        aligned to the forecast horizon. Series inputs are coerced to
        single-column DataFrames.

    Raises
    ------
    TypeError
        If `exog` is a long-format DataFrame whose second MultiIndex
        level is not a `DatetimeIndex`, or if `exog` is an unsupported
        type.
    ValueError
        If a non-DatetimeIndex exog has fewer than `steps` rows, or if
        a `RangeIndex` exog does not start at the expected position.

    """

    # Early return: no exog provided
    if exog is None:
        return {name: None for name in series_names_in}

    # Type guard
    if not isinstance(exog, (pd.Series, pd.DataFrame, dict)):
        raise TypeError(
            f"`exog` must be a pandas Series, DataFrame, dict, or None. "
            f"Got {type(exog)}."
        )

    # Normalize any input format (Series, DataFrame, dict) into a
    # per-series dict keyed by series name.
    exog_dict = self._exog_to_dict(exog, series_names_in)

    # Determine index type and freq once from the first context series.
    # All series share the same type and freq, and are non-empty with a
    # valid freq/step (guaranteed by check_preprocess_series upstream).
    first_ctx = next(iter(context.values()))
    is_datetime_ctx = isinstance(first_ctx.index, pd.DatetimeIndex)
    freq = first_ctx.index.freq if is_datetime_ctx else first_ctx.index.step

    # Align each series' exog to its forecast horizon
    exog_aligned = {}
    nan_filled_series = []
    for name in series_names_in:
        e = exog_dict.get(name)
        if e is None:
            exog_aligned[name] = None
            continue

        if isinstance(e, pd.Series):
            e = e.to_frame()

        ctx = context[name]
        ref_end = ctx.index[-1]
        label = f"`exog` for series '{name}'"

        # DatetimeIndex: reindex to the exact expected date range,
        # filling gaps with NaN.
        if is_datetime_ctx and isinstance(e.index, pd.DatetimeIndex):
            expected_idx = pd.date_range(
                start=ref_end + freq, periods=steps, freq=freq
            )
            e_aligned = e.reindex(expected_idx)
            if e_aligned.isnull().any(axis=None):
                nan_filled_series.append(name)
            exog_aligned[name] = e_aligned
        else:
            # RangeIndex / other: length check + optional start validation,
            # then truncate to the forecast horizon.
            if len(e) < steps:
                raise ValueError(
                    f"{label} must have at least {steps} values. "
                    f"Got {len(e)}."
                )
            if isinstance(e.index, pd.RangeIndex):
                expected_start = ref_end + freq
                if e.index[0] != expected_start:
                    raise ValueError(
                        f"To make predictions {label} must start one step "
                        f"ahead of `context`.\n"
                        f"    `context` ends at: {ref_end}.\n"
                        f"    {label} starts at: {e.index[0]}.\n"
                        f"    Expected index: {expected_start}."
                    )
            exog_aligned[name] = e.iloc[:steps]

    # Batch warning for all series whose exog had missing timestamps
    if nan_filled_series:
        warnings.warn(
            f"`exog` for series {nan_filled_series} has been reindexed "
            f"to match the expected forecast horizon. Missing timestamps "
            f"were filled with NaN.",
            MissingValuesWarning,
            stacklevel=4,
        )

    return exog_aligned

predict 

predict(
    steps,
    levels=None,
    context=None,
    context_exog=None,
    exog=None,
    quantiles=None,
    check_inputs=True,
)

Predict n steps ahead.

Parameters:

Name Type Description Default
steps int

Number of steps ahead to forecast.

 required
levels (str, list)

Subset of series to predict. If None, all series in context are predicted.

 None
context pandas Series, pandas DataFrame, dict

Override the stored context with this window.

  • If pandas Series: single-series override.
  • If wide pandas DataFrame or dict[str, pandas Series]: multi-series override.
 None
context_exog pandas Series, pandas DataFrame, dict

Historical exog corresponding to context.

 None
exog pandas Series, pandas DataFrame, dict

Future known exogenous variables for the forecast horizon.

  • If pandas Series or pandas DataFrame: broadcast to all series.
  • If dict: per-series exogenous variables.
 None
quantiles (list, tuple)

Quantile levels to return, e.g. [0.1, 0.5, 0.9]. If None, returns a point forecast (median).

 None
check_inputs bool

If True, the context and context_exog inputs are validated and normalized via _check_preprocess_context. If False, context must already be a dict[str, pandas Series] and context_exog must be a dict[str, pandas DataFrame | None] or None. This argument is created for internal use and is not recommended to be changed.

 True

Returns:

Name Type Description
predictions pandas DataFrame

Value of predictions. The DataFrame includes the following columns:

  • level: Name of the series.
  • pred: Predicted values (point forecast, median).

If quantiles is not None, the pred column is replaced by one column per quantile level (e.g., q_0.1, q_0.5, q_0.9).
Notes

Foundation models are pre-trained and do not learn from the data passed to fit. The fit method only stores context (the last context_length observations) and metadata. This leads to four distinct behaviors depending on the combination of is_fitted and context:

  • Not fitted, context=None: raises ValueError. There is no context available for prediction.
  • Fitted, context=None: uses the context and context_exog_ stored during fit. If the user supplies context_exog, it is ignored with a warning.
  • Not fitted, context provided (zero-shot mode): The model uses context and context_exog (if provided) as context for prediction.
  • Fitted, context provided: Stored context is ignored, the provided context and context_exog (if provided) are used for prediction.
Source code in skforecast/foundation/_foundation_model.py 
 804
 805
 806
 807
 808
 809
 810
 811
 812
 813
 814
 815
 816
 817
 818
 819
 820
 821
 822
 823
 824
 825
 826
 827
 828
 829
 830
 831
 832
 833
 834
 835
 836
 837
 838
 839
 840
 841
 842
 843
 844
 845
 846
 847
 848
 849
 850
 851
 852
 853
 854
 855
 856
 857
 858
 859
 860
 861
 862
 863
 864
 865
 866
 867
 868
 869
 870
 871
 872
 873
 874
 875
 876
 877
 878
 879
 880
 881
 882
 883
 884
 885
 886
 887
 888
 889
 890
 891
 892
 893
 894
 895
 896
 897
 898
 899
 900
 901
 902
 903
 904
 905
 906
 907
 908
 909
 910
 911
 912
 913
 914
 915
 916
 917
 918
 919
 920
 921
 922
 923
 924
 925
 926
 927
 928
 929
 930
 931
 932
 933
 934
 935
 936
 937
 938
 939
 940
 941
 942
 943
 944
 945
 946
 947
 948
 949
 950
 951
 952
 953
 954
 955
 956
 957
 958
 959
 960
 961
 962
 963
 964
 965
 966
 967
 968
 969
 970
 971
 972
 973
 974
 975
 976
 977
 978
 979
 980
 981
 982
 983
 984
 985
 986
 987
 988
 989
 990
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
def predict(
    self,
    steps: int,
    levels: str | list[str] | None = None,
    context: pd.Series | pd.DataFrame | dict[str, pd.Series] | None = None,
    context_exog: (
        pd.Series
        | pd.DataFrame
        | dict[str, pd.DataFrame | pd.Series | None]
        | None
    ) = None,
    exog: (
        pd.Series
        | pd.DataFrame
        | dict[str, pd.DataFrame | pd.Series | None]
        | None
    ) = None,
    quantiles: list[float] | tuple[float] | None = None,
    check_inputs: bool = True,
) -> pd.DataFrame:
    """
    Predict n steps ahead.

    Parameters
    ----------
    steps : int
        Number of steps ahead to forecast.
    levels : str, list, default None
        Subset of series to predict. If `None`, all series in `context` are 
        predicted. 
    context : pandas Series, pandas DataFrame, dict, default None
        Override the stored context with this window.

        - If `pandas Series`: single-series override.
        - If wide `pandas DataFrame` or `dict[str, pandas Series]`:
        multi-series override.
    context_exog : pandas Series, pandas DataFrame, dict, default None
        Historical exog corresponding to `context`.
    exog : pandas Series, pandas DataFrame, dict, default None
        Future known exogenous variables for the forecast horizon.

        - If `pandas Series` or `pandas DataFrame`: broadcast to all
        series.
        - If `dict`: per-series exogenous variables.
    quantiles : list, tuple, default None
        Quantile levels to return, e.g. `[0.1, 0.5, 0.9]`. If `None`,
        returns a point forecast (median).
    check_inputs : bool, default True
        If `True`, the `context` and `context_exog` inputs are validated
        and normalized via `_check_preprocess_context`. If `False`,
        `context` must already be a `dict[str, pandas Series]` and
        `context_exog` must be a `dict[str, pandas DataFrame | None]`
        or `None`. This argument is created for internal use and is not
        recommended to be changed.

    Returns
    -------
    predictions : pandas DataFrame
        Value of predictions. The DataFrame includes the following columns:

        - level: Name of the series.
        - pred: Predicted values (point forecast, median).

        If `quantiles` is not `None`, the `pred` column is replaced by
        one column per quantile level (e.g., `q_0.1`, `q_0.5`, `q_0.9`).

    Notes
    -----
    Foundation models are pre-trained and do not learn from the data passed 
    to `fit`. The `fit` method only stores context (the last `context_length` 
    observations) and metadata. This leads to four distinct behaviors 
    depending on the combination of `is_fitted` and `context`:

    - **Not fitted, `context=None`**: raises `ValueError`. There is no context 
    available for prediction.
    - **Fitted, `context=None`**: uses the context and `context_exog_` stored 
    during `fit`. If the user supplies `context_exog`, it is ignored with a 
    warning.
    - **Not fitted, `context` provided (zero-shot mode)**: The model uses 
    `context` and `context_exog` (if provided) as context for prediction.
    - **Fitted, `context` provided**: Stored context is ignored, the 
    provided `context` and `context_exog` (if provided) are used for 
    prediction.

    """

    if not self.is_fitted and context is None:
        raise ValueError(
            "Call `fit` before `predict`, or pass `context`."
        )

    if (
        isinstance(steps, bool)
        or not isinstance(steps, (int, np.integer))
        or steps < 1
    ):
        raise ValueError("`steps` must be a positive integer.")

    if quantiles is not None:
        if not isinstance(quantiles, (list, tuple)):
            raise TypeError(
                "`quantiles` must be a `list` or `tuple`. For example, quantiles "
                "0.1, 0.5, and 0.9 should be as `quantiles = [0.1, 0.5, 0.9]`."
            )
        for q in quantiles:
            if not 0.0 <= q <= 1.0:
                raise ValueError(
                    f"All quantiles must be between 0 and 1. Got {q}."
                )

    # Context (past data)
    if context is None:
        if context_exog is not None:
            warnings.warn(
                "`context_exog` is ignored when `context` is not provided. "
                "The stored `context_exog_` from `fit` is used instead.",
                IgnoredArgumentWarning,
                stacklevel=3,
            )
        context = self.adapter.context_
        series_names_in = self.series_names_in_
        context_exog = self.adapter.context_exog_
    elif check_inputs:
        context, _, series_names_in, context_exog, _ = self._check_preprocess_context(
            series = context,
            exog   = context_exog,
        )
    else:
        if not context:
            raise ValueError("`context` cannot be an empty dictionary.")
        series_names_in = list(context.keys())

    if levels is not None:
        if len(levels) == 0:
            raise ValueError("`levels` must be a single string or a list-like of strings, but cannot be empty.")
        requested_levels = [levels] if isinstance(levels, str) else list(levels)
        unknown = [lv for lv in requested_levels if lv not in series_names_in]
        if unknown:
            raise ValueError(
                f"`levels` {unknown} not found in available series "
                f"{list(series_names_in)}."
            )
        series_names_in = requested_levels
        context = {name: context[name] for name in requested_levels}
        if context_exog is not None:
            context_exog = {
                name: context_exog.get(name) for name in requested_levels
            }

    # Future exog
    if not self.allow_exog:
        has_exog = (exog is not None) or (context_exog is not None)
        if has_exog:
            warnings.warn(
                f"{type(self.adapter).__name__} does not currently "
                "support covariates. `exog` and `context_exog` "
                "are ignored.",
                IgnoredArgumentWarning,
                stacklevel=3,
            )
            exog = None
            context_exog = None
    else:
        if check_inputs:
            exog = self._prepare_future_exog(
                       steps           = steps,
                       context         = context,
                       exog            = exog,
                       series_names_in = series_names_in,
                   )

    # Adapter returns dict[str, np.ndarray] with shape (steps, n_q)
    raw_predictions = self.adapter.predict(
                          steps        = steps,
                          context      = context,
                          context_exog = context_exog,
                          exog         = exog,
                          quantiles    = quantiles,
                      )

    # Build long-format DataFrame from raw predictions
    n_series = len(series_names_in)
    per_series_indices = [
        expand_index(context[name].index, steps=steps)
        for name in series_names_in
    ]

    if n_series == 1:
        long_index = per_series_indices[0]
    else:
        idx_arr = np.column_stack(
            [idx.to_numpy() for idx in per_series_indices]
        ).ravel()
        long_index = (
            pd.DatetimeIndex(idx_arr)
            if isinstance(per_series_indices[0], pd.DatetimeIndex)
            else pd.Index(idx_arr)
        )
    level_col = np.tile(series_names_in, steps)

    col_names = ["pred"] if quantiles is None else [f"q_{q}" for q in quantiles]
    n_cols = len(col_names)
    # Pre-allocate (steps, n_series, n_cols), fill per series, then reshape
    # to step-major (steps*n_series, n_cols) — one allocation instead of one
    # per quantile, and the ravel order matches level_col / long_index.
    pred_matrix = np.empty((steps, n_series, n_cols), dtype=np.float64)
    for i, name in enumerate(series_names_in):
        pred_matrix[:, i, :] = raw_predictions[name]
    pred_matrix = pred_matrix.reshape(steps * n_series, n_cols)
    predictions: dict[str, np.ndarray] = {"level": level_col}
    for j, col in enumerate(col_names):
        predictions[col] = pred_matrix[:, j]

    predictions = pd.DataFrame(predictions, index=long_index)

    return predictions

get_params 

get_params(deep=True)

Get parameters for this estimator (sklearn-compatible).

Parameters:

Name Type Description Default
deep bool

Not used, present here for API consistency by convention.

 True

Returns:

Name Type Description
params dict

Parameter names mapped to their current values.
Notes

Required so that sklearn.base.clone can create an unfitted copy of this object. clone is invoked when a ForecasterFoundation is constructed (in its __init__) and by deepcopy_forecaster during model selection and hyperparameter search. The pre-loaded pipeline is intentionally excluded so that clones are created without copying heavy model weights; the pipeline is reloaded lazily on the first predict call.

Source code in skforecast/foundation/_foundation_model.py 
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
def get_params(self, deep: bool = True) -> dict:
    """
    Get parameters for this estimator (sklearn-compatible).

    Parameters
    ----------
    deep : bool, default True
        Not used, present here for API consistency by convention.

    Returns
    -------
    params : dict
        Parameter names mapped to their current values.

    Notes
    -----
    Required so that `sklearn.base.clone` can create an unfitted copy of
    this object. `clone` is invoked when a `ForecasterFoundation` is
    constructed (in its `__init__`) and by `deepcopy_forecaster` during
    model selection and hyperparameter search. The pre-loaded pipeline is
    intentionally excluded so that clones are created without copying heavy
    model weights; the pipeline is reloaded lazily on the first `predict`
    call.

    """

    return self.adapter.get_params()

set_params 

set_params(**params)

Set parameters for this estimator (sklearn-compatible).

After calling this method, the FoundationModel is reset to an unfitted state.

Parameters:

Name Type Description Default
**params

Estimator parameters forwarded to the underlying adapter's set_params. Use model_id to change the model ID. All other keys are adapter-specific.

 {}

Returns:

Name Type Description
self FoundationModel

The same object with updated parameters.
Source code in skforecast/foundation/_foundation_model.py 
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
def set_params(self, **params) -> FoundationModel:
    """
    Set parameters for this estimator (sklearn-compatible).

    After calling this method, the FoundationModel is reset to an unfitted state.

    Parameters
    ----------
    **params :
        Estimator parameters forwarded to the underlying adapter's
        `set_params`. Use `model_id` to change the model ID. All
        other keys are adapter-specific.

    Returns
    -------
    self : FoundationModel
        The same object with updated parameters.

    """

    try:
        self.adapter.set_params(**params)
    except ValueError as exc:
        adapter_name = type(self.adapter).__name__
        message = str(exc).replace(
            f"Invalid parameter(s) for {adapter_name}:",
            "Invalid parameter(s) for FoundationModel:",
        )
        raise ValueError(message) from exc

    self.index_type_               = None
    self.index_freq_               = None
    self.context_range_            = None
    self.series_names_in_          = None
    self.is_multiple_series_       = False
    self.exog_in_                  = False
    self.exog_names_in_            = None
    self.exog_names_in_per_series_ = None
    self.exog_type_in_             = None
    self.fit_date                  = None
    self.adapter.context_          = None
    self.adapter.context_exog_     = None
    self.adapter.is_fitted         = False

    return self

skforecast.foundation._adapters.ChronosAdapter 

ChronosAdapter(
    model_id,
    *,
    pipeline=None,
    context_length=8192,
    predict_kwargs=None,
    device_map="auto",
    torch_dtype=None,
    cross_learning=False
)

Adapter for Amazon Chronos foundation models.

Parameters:

Name Type Description Default
model_id str

HuggingFace model ID, e.g. "autogluon/chronos-2-small".

 required
pipeline BaseChronosPipeline

Pre-loaded pipeline instance. If None, the pipeline is loaded lazily on the first call to predict.

 None
context_length int

Maximum number of historical observations to use as context. At fit time only the last context_length observations are stored. At predict time, if context is longer than context_length it is trimmed to this length; if it is shorter, all available observations are used as-is. Defaults to 8192, which matches the maximum context window of Chronos. Must be a positive integer.

 8192
predict_kwargs dict

Additional keyword arguments forwarded to the pipeline's predict_quantiles method.

 None
device_map str

Device placement for the model. "auto" selects the best available accelerator (CUDA > MPS > CPU). Also accepts explicit values such as "cuda", "mps", or "cpu", forwarded to BaseChronosPipeline.from_pretrained.

 'auto'
torch_dtype object

Torch dtype forwarded to BaseChronosPipeline.from_pretrained.

 None
cross_learning bool

If True, Chronos shares information across all series in the batch when predicting in multi-series mode. Forwarded directly to predict_quantiles. Ignored in single-series mode.

 False

Attributes:

Name Type Description
model_id str

HuggingFace model ID.
context_ dict

Stored training series after fitting.
context_exog_ dict

Stored historical exogenous variables after fitting.
context_length int

Maximum number of historical observations used as context.
predict_kwargs dict

Additional keyword arguments forwarded to predict_quantiles.
device_map str

Device map string for model loading.
torch_dtype object

Torch dtype for model loading.
cross_learning bool

Whether cross-series learning is enabled.
is_fitted bool

Whether the adapter has been fitted.
References

.. [1] https://github.com/amazon-science/chronos-forecasting

.. [2] https://huggingface.co/amazon/chronos-2

Initialise the adapter.

Parameters:

Name Type Description Default
model_id str

HuggingFace model ID, e.g. "autogluon/chronos-2-small".

 required
pipeline BaseChronosPipeline

Pre-loaded pipeline instance. If None, the pipeline is loaded lazily on the first call to predict.

 None
context_length int

Maximum number of historical observations to retain as context. At fit time only the last context_length observations of series (and exog) are stored. At predict time, if context is longer than context_length it is trimmed to this length before inference; if it is shorter, all available observations are passed as-is and the model handles reduced context gracefully. Defaults to 8192, which matches the maximum context window of Chronos. Must be a positive integer.

 8192
predict_kwargs dict

Additional keyword arguments forwarded verbatim to the pipeline's predict_quantiles method.

 None
device_map str

Device placement for the model. "auto" selects the best available accelerator (CUDA > MPS > CPU). Also accepts explicit values such as "cuda", "mps", or "cpu", forwarded to BaseChronosPipeline.from_pretrained.

 'auto'
torch_dtype object

Torch dtype forwarded to BaseChronosPipeline.from_pretrained (e.g. torch.bfloat16).

 None
cross_learning bool

If True, Chronos shares information across all series in the batch when predicting in multi-series mode. Forwarded directly to predict_quantiles. Ignored in single-series mode.

 False

Methods:

Name Description
get_params

Return the adapter's constructor parameters.
set_params

Set adapter parameters. Resets the pipeline when model_id,
fit

Store the training series and optional historical exogenous variables.
predict

Generate predictions using the Chronos pipeline.
Source code in skforecast/foundation/_adapters.py 
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
def __init__(
    self,
    model_id: str,
    *,
    pipeline: Any | None = None,
    context_length: int = 8192,
    predict_kwargs: dict[str, Any] | None = None,
    device_map: str = "auto",
    torch_dtype: Any | None = None,
    cross_learning: bool = False,
) -> None:
    """
    Initialise the adapter.

    Parameters
    ----------
    model_id : str
        HuggingFace model ID, e.g. "autogluon/chronos-2-small".
    pipeline : BaseChronosPipeline, default None
        Pre-loaded pipeline instance. If `None`, the pipeline is
        loaded lazily on the first call to `predict`.
    context_length : int, default 8192
        Maximum number of historical observations to retain as context.
        At `fit` time only the last `context_length` observations of
        `series` (and `exog`) are stored. At `predict` time, if
        `context` is longer than `context_length` it is trimmed to
        this length before inference; if it is shorter, all available
        observations are passed as-is and the model handles reduced
        context gracefully. Defaults to 8192, which matches the
        maximum context window of Chronos. Must be a positive
        integer.
    predict_kwargs : dict, default None
        Additional keyword arguments forwarded verbatim to the
        pipeline's `predict_quantiles` method.
    device_map : str, default 'auto'
        Device placement for the model. `"auto"` selects the best
        available accelerator (CUDA > MPS > CPU). Also accepts
        explicit values such as `"cuda"`, `"mps"`, or `"cpu"`,
        forwarded to `BaseChronosPipeline.from_pretrained`.
    torch_dtype : object, default None
        Torch dtype forwarded to `BaseChronosPipeline.from_pretrained`
        (e.g. `torch.bfloat16`).
    cross_learning : bool, default False
        If `True`, Chronos shares information across all series in
        the batch when predicting in multi-series mode. Forwarded
        directly to `predict_quantiles`. Ignored in single-series mode.

    """

    _validate_positive_int("context_length", context_length)

    self.model_id       = model_id
    self._pipeline      = pipeline
    self.context_       = None
    self.context_exog_  = None
    self.context_length = context_length
    self.predict_kwargs = predict_kwargs or {}
    self.device_map     = device_map
    self.torch_dtype    = torch_dtype
    self.cross_learning = cross_learning
    self.is_fitted      = False

Attributes

allow_exog class-attribute instance-attribute 

allow_exog = True

model_id instance-attribute 

model_id = model_id

_pipeline instance-attribute 

_pipeline = pipeline

context_ instance-attribute 

context_ = None

context_exog_ instance-attribute 

context_exog_ = None

context_length instance-attribute 

context_length = context_length

predict_kwargs instance-attribute 

predict_kwargs = predict_kwargs or {}

device_map instance-attribute 

device_map = device_map

torch_dtype instance-attribute 

torch_dtype = torch_dtype

cross_learning instance-attribute 

cross_learning = cross_learning

is_fitted instance-attribute 

is_fitted = False

Methods:

get_params 

get_params()

Return the adapter's constructor parameters.

Returns:

Name Type Description
params dict

Keys: model_id, cross_learning, context_length, device_map, torch_dtype, predict_kwargs.
Source code in skforecast/foundation/_adapters.py 
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
def get_params(self) -> dict:
    """
    Return the adapter's constructor parameters.

    Returns
    -------
    params : dict
        Keys: `model_id`, `cross_learning`, `context_length`,
        `device_map`, `torch_dtype`, `predict_kwargs`.

    """
    return {
        'model_id':       self.model_id,
        'cross_learning': self.cross_learning,
        'context_length': self.context_length,
        'device_map':     self.device_map,
        'torch_dtype':    self.torch_dtype,
        'predict_kwargs': self.predict_kwargs or None,
    }

set_params 

set_params(**params)

Set adapter parameters. Resets the pipeline when model_id, device_map, or torch_dtype changes, since those are baked into the loaded pipeline.

Parameters:

Name Type Description Default
**params

Valid keys: model_id, cross_learning, context_length, device_map, torch_dtype, predict_kwargs.

 {}

Returns:

Name Type Description
self ChronosAdapter
Source code in skforecast/foundation/_adapters.py 
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
def set_params(self, **params) -> ChronosAdapter:
    """
    Set adapter parameters. Resets the pipeline when `model_id`,
    `device_map`, or `torch_dtype` changes, since those are baked into the
    loaded pipeline.

    Parameters
    ----------
    **params :
        Valid keys: `model_id`, `cross_learning`, `context_length`,
        `device_map`, `torch_dtype`, `predict_kwargs`.

    Returns
    -------
    self : ChronosAdapter

    """

    def validate(p: dict) -> dict:
        if "context_length" in p:
            _validate_positive_int("context_length", p["context_length"])
        if "predict_kwargs" in p:
            p["predict_kwargs"] = p["predict_kwargs"] or {}
        return p

    return _apply_set_params(
        self, params,
        validate=validate,
        resets=(
            (
                {"model_id", "device_map", "torch_dtype"},
                lambda: setattr(self, "_pipeline", None),
            ),
        ),
    )

fit 

fit(context, context_exog)

Store the training series and optional historical exogenous variables. No model training occurs since Chronos is a zero-shot inference model.

All input normalization and validation is performed upstream by FoundationModel; this method receives canonical dicts only.

Parameters:

Name Type Description Default
context dict pandas Series

Normalized training series, one entry per series.

 required
context_exog dict pandas DataFrame, pandas Series, or None

Per-series historical exogenous variables (past covariates).

 required

Returns:

Name Type Description
self ChronosAdapter
Source code in skforecast/foundation/_adapters.py 
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
def fit(
    self,
    context: dict[str, pd.Series],
    context_exog: dict[str, pd.DataFrame | pd.Series | None],
) -> ChronosAdapter:
    """
    Store the training series and optional historical exogenous variables.
    No model training occurs since Chronos is a zero-shot inference model.

    All input normalization and validation is performed upstream by
    `FoundationModel`; this method receives canonical dicts only.

    Parameters
    ----------
    context : dict pandas Series
        Normalized training series, one entry per series.
    context_exog : dict pandas DataFrame, pandas Series, or None
        Per-series historical exogenous variables (past covariates).

    Returns
    -------
    self : ChronosAdapter

    """

    self.context_ = context
    self.context_exog_ = context_exog
    self.is_fitted = True

    return self

predict 

predict(steps, context, context_exog, exog, quantiles)

Generate predictions using the Chronos pipeline.

All input normalization, validation, and context trimming is performed upstream by FoundationModel; this method receives pre-processed dicts only.

Parameters:

Name Type Description Default
steps int

Number of steps ahead to forecast.

 required
context dict

Per-series context windows (already trimmed to context_length).

 required
context_exog dict

Per-series past covariates (already trimmed).

 required
exog dict

Per-series future covariates for the forecast horizon.

 required
quantiles list of float or None

Quantile levels to return. If None, a point forecast (median, quantile 0.5) is produced.

 required

Returns:

Name Type Description
predictions dict

Keys are series names. Each value is a 2-D array of shape (steps, n_quantiles).
Source code in skforecast/foundation/_adapters.py 
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
def predict(
    self,
    steps: int,
    context: dict[str, pd.Series],
    context_exog: dict[str, pd.DataFrame | pd.Series | None],
    exog: dict[str, pd.DataFrame | pd.Series | None],
    quantiles: list[float] | tuple[float] | None
) -> dict[str, np.ndarray]:
    """
    Generate predictions using the Chronos pipeline.

    All input normalization, validation, and context trimming is
    performed upstream by `FoundationModel`; this method receives
    pre-processed dicts only.

    Parameters
    ----------
    steps : int
        Number of steps ahead to forecast.
    context : dict
        Per-series context windows (already trimmed to
        `context_length`).
    context_exog : dict
        Per-series past covariates (already trimmed).
    exog : dict
        Per-series future covariates for the forecast horizon.
    quantiles : list of float or None
        Quantile levels to return. If `None`, a point forecast
        (median, quantile 0.5) is produced.

    Returns
    -------
    predictions : dict
        Keys are series names. Each value is a 2-D array of shape
        `(steps, n_quantiles)`.

    """

    # NOTE: the pipeline is loaded lazily here so that the adapter can be
    # instantiated and fitted without requiring Chronos to be installed.
    self._load_pipeline()

    series_names_in = list(context.keys())
    quantile_levels = list(quantiles) if quantiles is not None else [0.5]

    inputs_list = [
        self._build_chronos_input(
            context      = context[name].to_numpy(),
            context_exog = context_exog[name] if context_exog is not None else None,
            exog         = exog[name] if exog is not None else None,
        )
        for name in series_names_in
    ]

    quantile_preds, _ = self._pipeline.predict_quantiles(
        inputs            = inputs_list,
        prediction_length = steps,
        quantile_levels   = quantile_levels,
        cross_learning    = self.cross_learning if len(series_names_in) > 1 else False,
        **self.predict_kwargs,
    )

    predictions: dict[str, np.ndarray] = {}
    for i, name in enumerate(series_names_in):
        q_arr = _tensor_to_numpy(quantile_preds[i].squeeze(0))
        predictions[name] = q_arr

    return predictions

_load_pipeline 

_load_pipeline()

Load the Chronos pipeline into self._pipeline if not already set.

Returns:

Type Description
None

Raises:

Type Description
ImportError

If chronos-forecasting >=2.0 is not installed.
Notes

The pipeline is imported lazily from chronos and instantiated via BaseChronosPipeline.from_pretrained, which auto-dispatches to the correct pipeline class based on the model config. Optional device_map and torch_dtype stored at initialisation are forwarded to the constructor. This method is a no-op when self._pipeline is already populated.

Source code in skforecast/foundation/_adapters.py 
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
def _load_pipeline(self) -> None:
    """
    Load the Chronos pipeline into `self._pipeline` if not already set.

    Returns
    -------
    None

    Raises
    ------
    ImportError
        If `chronos-forecasting` >=2.0 is not installed.

    Notes
    -----
    The pipeline is imported lazily from `chronos` and instantiated via
    `BaseChronosPipeline.from_pretrained`, which auto-dispatches to the
    correct pipeline class based on the model config. Optional
    `device_map` and `torch_dtype` stored at initialisation are
    forwarded to the constructor. This method is a no-op when
    `self._pipeline` is already populated.

    """

    if self._pipeline is not None:
        return
    try:
        from chronos import BaseChronosPipeline
    except ImportError as exc:
        raise ImportError(
            "chronos-forecasting >=2.0 is required. "
            "Install it with `pip install chronos-forecasting`."
        ) from exc

    kwargs: dict[str, Any] = {}
    kwargs["device_map"] = self.device_map
    if self.torch_dtype is not None:
        kwargs["torch_dtype"] = self.torch_dtype

    self._pipeline = BaseChronosPipeline.from_pretrained(self.model_id, **kwargs)

_to_covariate_array staticmethod 

_to_covariate_array(col_data)

Convert a covariate column to a numpy array.

Numeric columns (int, float) and boolean columns are cast to float32. All other dtypes (object, string, Categorical) are left as-is so that Chronos can handle them as categorical covariates natively.

Parameters:

Name Type Description Default
col_data array - like

A single covariate column (e.g. a pandas Series or 1-D array).

 required

Returns:

Name Type Description
col_array numpy ndarray

A 1-D numpy array. Numeric/bool are cast to float32. Others keep their original dtype (typically object for string and categorical data).
Source code in skforecast/foundation/_adapters.py 
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
@staticmethod
def _to_covariate_array(col_data: Any) -> np.ndarray:
    """
    Convert a covariate column to a numpy array.

    Numeric columns (int, float) and boolean columns are cast to
    `float32`. All other dtypes (object, string, Categorical) are left
    as-is so that Chronos can handle them as categorical covariates
    natively.

    Parameters
    ----------
    col_data : array-like
        A single covariate column (e.g. a pandas Series or 1-D array).

    Returns
    -------
    col_array : numpy ndarray
        A 1-D numpy array. Numeric/bool are cast to `float32`. Others
        keep their original dtype (typically `object` for string and
        categorical data).

    """

    # Handle pandas Series first to correctly process nullable extension
    # dtypes (pd.Int64Dtype, pd.Float64Dtype, pd.BooleanDtype): np.asarray()
    # on those produces dtype=object with pd.NA sentinels instead of float32.
    if isinstance(col_data, pd.Series):
        if pd.api.types.is_numeric_dtype(col_data) or pd.api.types.is_bool_dtype(col_data):
            return col_data.astype(np.float32).to_numpy()
        return col_data.to_numpy()

    # Fallback for numpy arrays, lists, etc.
    arr = np.asarray(col_data)
    if arr.dtype.kind in ("i", "u", "f", "b"):  # integer, unsigned int, float, bool
        return arr.astype(np.float32)

    return arr

_build_chronos_input 

_build_chronos_input(context, context_exog=None, exog=None)

Build the input dict consumed by the pipeline's predict_quantiles method.

Parameters:

Name Type Description Default
context numpy ndarray

1-D array of observed time series values used as context. Must be castable to float32.

 required
context_exog pandas DataFrame, pandas Series

Historical exogenous variables whose index is aligned to context. Each column (or the single Series, referenced by its name) becomes an entry in the returned "past_covariates" dict. Numeric and boolean columns are cast to float32; string and categorical columns are passed as-is and handled natively by Chronos.

 None
exog pandas DataFrame, pandas Series

Future-known exogenous variables covering the forecast horizon. Must have exactly prediction_length rows. Each column becomes an entry in the returned "future_covariates" dict. Numeric and boolean columns are cast to float32; string and categorical columns are passed as-is.

 None

Returns:

Name Type Description
input_dict dict

Dictionary with mandatory key "target" (1-D float32 numpy ndarray) and optional keys "past_covariates" and "future_covariates", each mapping column names to 1-D arrays (float32 for numeric/bool columns, object dtype for string/categorical columns).
Source code in skforecast/foundation/_adapters.py 
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
def _build_chronos_input(
    self,
    context: np.ndarray,
    context_exog: pd.DataFrame | pd.Series | None = None,
    exog: pd.DataFrame | pd.Series | None = None,
) -> dict[str, Any]:
    """
    Build the input dict consumed by the pipeline's `predict_quantiles` method.

    Parameters
    ----------
    context : numpy ndarray
        1-D array of observed time series values used as context. Must be
        castable to `float32`.
    context_exog : pandas DataFrame, pandas Series, default None
        Historical exogenous variables whose index is aligned to
        `context`. Each column (or the single Series, referenced by
        its name) becomes an entry in the returned
        "past_covariates" dict. Numeric and boolean columns are
        cast to `float32`; string and categorical columns are passed
        as-is and handled natively by Chronos.
    exog : pandas DataFrame, pandas Series, default None
        Future-known exogenous variables covering the forecast horizon.
        Must have exactly `prediction_length` rows. Each column
        becomes an entry in the returned "future_covariates" dict.
        Numeric and boolean columns are cast to `float32`; string and
        categorical columns are passed as-is.

    Returns
    -------
    input_dict : dict
        Dictionary with mandatory key "target" (1-D `float32`
        `numpy ndarray`) and optional keys "past_covariates" and
        "future_covariates", each mapping column names to 1-D
        arrays (`float32` for numeric/bool columns, `object` dtype
        for string/categorical columns).

    """

    input_dict = {"target": np.asarray(context, dtype=np.float32)}
    if context_exog is not None:
        df = (
            context_exog
            if isinstance(context_exog, pd.DataFrame)
            else context_exog.to_frame()
        )
        input_dict["past_covariates"] = {
            col: ChronosAdapter._to_covariate_array(df[col]) for col in df.columns
        }
    if exog is not None:
        df = (
            exog
            if isinstance(exog, pd.DataFrame)
            else exog.to_frame()
        )
        input_dict["future_covariates"] = {
            col: ChronosAdapter._to_covariate_array(df[col]) for col in df.columns
        }

    return input_dict

skforecast.foundation._adapters.TimesFMAdapter 

TimesFMAdapter(
    model_id,
    *,
    model=None,
    context_length=512,
    max_horizon=512,
    forecast_config_kwargs=None
)

Adapter for Google TimesFM foundation models.

Parameters:

Name Type Description Default
model_id str

HuggingFace model ID, e.g. "google/timesfm-2.5-200m-pytorch".

 required
model object

Pre-loaded and compiled TimesFM model instance. If None, the model is loaded and compiled lazily on the first predict call.

 None
context_length int

Maximum number of historical observations to use as context. At fit time only the last context_length observations are stored. At predict time, if context is longer than context_length it is trimmed to this length; if it is shorter, all available observations are used as-is. Must be a positive integer. Defaults to 512. TimesFM supports up to 16_384.

 512
max_horizon int

Maximum forecast horizon. If predict is called with steps > max_horizon, a ValueError is raised. The model is compiled lazily for the exact requested steps (up to this ceiling) to avoid unnecessary decode iterations. Must be a positive integer.

 512
forecast_config_kwargs dict

Additional keyword arguments forwarded verbatim to timesfm.ForecastConfig at compile time. Supported keys: normalize_inputs, use_continuous_quantile_head, force_flip_invariance, infer_is_positive, fix_quantile_crossing. Do not include max_context or max_horizon here — those are controlled by the corresponding adapter parameters.

 None

Attributes:

Name Type Description
model_id str

HuggingFace model ID.
context_ dict

Stored training series after fitting.
context_exog_ dict

Not used, present here for API consistency by convention.
context_length int

Maximum number of historical observations used as context.
max_horizon int

Maximum forecast horizon.
forecast_config_kwargs dict

Additional keyword arguments forwarded to ForecastConfig.
is_fitted bool

Whether the adapter has been fitted.
Notes

TimesFM supports only the fixed quantile levels [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]. Requesting any other level raises a ValueError.

Covariate support (via TimesFM's forecast_with_covariates) is not yet implemented. Passing exog or context_exog issues an IgnoredArgumentWarning and the values are discarded.

Compilation behavior. The model is compiled lazily on the first predict call, sized for the exact number of steps requested (not for max_horizon, which only acts as an upper bound and validation ceiling). When steps is constant across calls, as in a typical backtesting loop, compilation happens only once, on the first fold. A later predict that requests more steps than any previous call triggers a single recompilation for the larger horizon. To avoid any runtime compilation altogether, pass an already-compiled model via the model argument.

References

.. [1] https://github.com/google-research/timesfm

.. [2] https://huggingface.co/google/timesfm-2.5-200m-pytorch

Initialise the adapter.

Parameters:

Name Type Description Default
model_id str

HuggingFace model ID, e.g. "google/timesfm-2.5-200m-pytorch".

 required
model object

Pre-loaded and compiled TimesFM model instance. If None, the model is loaded and compiled lazily on the first predict call.

 None
context_length int

Maximum number of historical observations to retain as context. At fit time only the last context_length observations of series are stored. At predict time, if context is longer than context_length it is trimmed to this length; if it is shorter, all available observations are passed as-is. Must be a positive integer.

 512
max_horizon int

Maximum forecast horizon. If predict is called with steps > max_horizon, a ValueError is raised. The model is compiled lazily for the exact requested steps (up to this ceiling) to avoid unnecessary decode iterations. Must be a positive integer.

 512
forecast_config_kwargs dict

Additional keyword arguments forwarded verbatim to timesfm.ForecastConfig at compile time.

 None

Methods:

Name Description
get_params

Return the adapter's constructor parameters.
set_params

Set adapter parameters. Resets the model when parameters that affect
fit

Store the training series.
predict

Generate predictions using the TimesFM model.
Source code in skforecast/foundation/_adapters.py 
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
def __init__(
    self,
    model_id: str,
    *,
    model: Any | None = None,
    context_length: int = 512,
    max_horizon: int = 512,
    forecast_config_kwargs: dict[str, Any] | None = None,
) -> None:
    """
    Initialise the adapter.

    Parameters
    ----------
    model_id : str
        HuggingFace model ID, e.g. "google/timesfm-2.5-200m-pytorch".
    model : object, default None
        Pre-loaded and compiled TimesFM model instance. If `None`, the
        model is loaded and compiled lazily on the first `predict` call.
    context_length : int, default 512
        Maximum number of historical observations to retain as context.
        At `fit` time only the last `context_length` observations of
        `series` are stored. At `predict` time, if `context` is
        longer than `context_length` it is trimmed to this length;
        if it is shorter, all available observations are passed as-is.
        Must be a positive integer.
    max_horizon : int, default 512
        Maximum forecast horizon. If `predict` is called with
        `steps > max_horizon`, a `ValueError` is raised. The model
        is compiled lazily for the exact requested `steps` (up to
        this ceiling) to avoid unnecessary decode iterations. Must
        be a positive integer.
    forecast_config_kwargs : dict, default None
        Additional keyword arguments forwarded verbatim to
        `timesfm.ForecastConfig` at compile time.

    """

    _validate_positive_int("context_length", context_length)
    _validate_positive_int("max_horizon", max_horizon)

    self.model_id               = model_id
    self._model                 = model
    self.context_               = None
    self.context_exog_          = None
    self.context_length         = context_length
    self.max_horizon            = max_horizon
    self.forecast_config_kwargs = forecast_config_kwargs or {}
    self.is_fitted              = False

Attributes

SUPPORTED_QUANTILES class-attribute instance-attribute 

SUPPORTED_QUANTILES = [
    0.1,
    0.2,
    0.3,
    0.4,
    0.5,
    0.6,
    0.7,
    0.8,
    0.9,
]

allow_exog class-attribute instance-attribute 

allow_exog = False

model_id instance-attribute 

model_id = model_id

_model instance-attribute 

_model = model

context_ instance-attribute 

context_ = None

context_exog_ instance-attribute 

context_exog_ = None

context_length instance-attribute 

context_length = context_length

max_horizon instance-attribute 

max_horizon = max_horizon

forecast_config_kwargs instance-attribute 

forecast_config_kwargs = forecast_config_kwargs or {}

is_fitted instance-attribute 

is_fitted = False

Methods:

get_params 

get_params()

Return the adapter's constructor parameters.

Returns:

Name Type Description
params dict

Keys: model_id, context_length, max_horizon, forecast_config_kwargs.
Source code in skforecast/foundation/_adapters.py 
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
def get_params(self) -> dict:
    """
    Return the adapter's constructor parameters.

    Returns
    -------
    params : dict
        Keys: `model_id`, `context_length`, `max_horizon`,
        `forecast_config_kwargs`.

    """
    return {
        'model_id':               self.model_id,
        'context_length':         self.context_length,
        'max_horizon':            self.max_horizon,
        'forecast_config_kwargs': self.forecast_config_kwargs or None,
    }

set_params 

set_params(**params)

Set adapter parameters. Resets the model when parameters that affect compilation change (model_id, context_length, max_horizon, forecast_config_kwargs).

Parameters:

Name Type Description Default
**params

Valid keys: model_id, context_length, max_horizon, forecast_config_kwargs.

 {}

Returns:

Name Type Description
self TimesFMAdapter
Source code in skforecast/foundation/_adapters.py 
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
def set_params(self, **params) -> TimesFMAdapter:
    """
    Set adapter parameters. Resets the model when parameters that affect
    compilation change (`model_id`, `context_length`, `max_horizon`,
    `forecast_config_kwargs`).

    Parameters
    ----------
    **params :
        Valid keys: `model_id`, `context_length`, `max_horizon`,
        `forecast_config_kwargs`.

    Returns
    -------
    self : TimesFMAdapter

    """

    def validate(p: dict) -> dict:
        if "context_length" in p:
            _validate_positive_int("context_length", p["context_length"])
        if "max_horizon" in p:
            _validate_positive_int("max_horizon", p["max_horizon"])
        if "forecast_config_kwargs" in p:
            p["forecast_config_kwargs"] = p["forecast_config_kwargs"] or {}
        return p

    return _apply_set_params(
        self, params,
        validate=validate,
        resets=(
            (
                {"model_id", "context_length", "max_horizon", "forecast_config_kwargs"},
                lambda: setattr(self, "_model", None),
            ),
        ),
    )

fit 

fit(context, context_exog)

Store the training series. No model training occurs since TimesFM is a zero-shot inference model.

All input normalization and validation is performed upstream by FoundationModel; this method receives canonical dicts only.

Parameters:

Name Type Description Default
context dict pandas Series

Normalized training series, one entry per series.

 required
context_exog Any

Not used, present here for API consistency by convention.

 required

Returns:

Name Type Description
self TimesFMAdapter
Source code in skforecast/foundation/_adapters.py 
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
def fit(
    self,
    context: dict[str, pd.Series],
    context_exog: Any,
) -> TimesFMAdapter:
    """
    Store the training series.
    No model training occurs since TimesFM is a zero-shot inference model.

    All input normalization and validation is performed upstream by
    `FoundationModel`; this method receives canonical dicts only.

    Parameters
    ----------
    context : dict pandas Series
        Normalized training series, one entry per series.
    context_exog : Any
        Not used, present here for API consistency by convention.

    Returns
    -------
    self : TimesFMAdapter

    """

    self.context_ = context
    self.is_fitted = True

    return self

predict 

predict(steps, context, context_exog, exog, quantiles)

Generate predictions using the TimesFM model.

All input normalization, validation, and context trimming is performed upstream by FoundationModel; this method receives pre-processed dicts only.

Parameters:

Name Type Description Default
steps int

Number of steps ahead to forecast.

 required
context dict

Per-series context windows (already trimmed to context_length).

 required
context_exog Any

Not used, present here for API consistency by convention.

 required
exog Any

Not used, present here for API consistency by convention.

 required
quantiles list of float or None

Quantile levels. Must be a subset of SUPPORTED_QUANTILES.

 required

Returns:

Name Type Description
predictions dict

Keys are series names. Each value is a 2-D array of shape (steps, n_quantiles).

Raises:

Type Description
ValueError

If a requested quantile level is not in SUPPORTED_QUANTILES or steps exceeds max_horizon.
Source code in skforecast/foundation/_adapters.py 
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
def predict(
    self,
    steps: int,
    context: dict[str, pd.Series],
    context_exog: Any,
    exog: Any,
    quantiles: list[float] | tuple[float] | None,
) -> dict[str, np.ndarray]:
    """
    Generate predictions using the TimesFM model.

    All input normalization, validation, and context trimming is
    performed upstream by `FoundationModel`; this method receives
    pre-processed dicts only.

    Parameters
    ----------
    steps : int
        Number of steps ahead to forecast.
    context : dict
        Per-series context windows (already trimmed to
        `context_length`).
    context_exog : Any
        Not used, present here for API consistency by convention.
    exog : Any
        Not used, present here for API consistency by convention.
    quantiles : list of float or None
        Quantile levels. Must be a subset of `SUPPORTED_QUANTILES`.

    Returns
    -------
    predictions : dict
        Keys are series names. Each value is a 2-D array of shape
        `(steps, n_quantiles)`.

    Raises
    ------
    ValueError
        If a requested quantile level is not in `SUPPORTED_QUANTILES`
        or `steps` exceeds `max_horizon`.

    """

    if quantiles is not None:
        quantile_list = list(quantiles)
        for q in quantile_list:
            if not any(abs(q - sq) < 1e-9 for sq in self.SUPPORTED_QUANTILES):
                raise ValueError(
                    f"TimesFM only supports quantile levels "
                    f"{self.SUPPORTED_QUANTILES}. Got {q!r}. "
                    f"Quantile interpolation is not supported."
                )
    else:
        quantile_list = None

    if steps > self.max_horizon:
        raise ValueError(
            f"`steps` ({steps}) exceeds `max_horizon` ({self.max_horizon})."
        )

    self._load_model()
    self._ensure_compiled(steps)

    series_names_in = list(context.keys())
    inputs_list = [
        context[name].to_numpy() for name in series_names_in
    ]

    point_forecast, quantile_forecast = self._model.forecast(
        horizon=steps,
        inputs=inputs_list,
    )
    # point_forecast  : (n_series, steps)
    # quantile_forecast: (n_series, steps, 10)  — idx 0 = mean, 1-9 = q0.1-q0.9

    predictions: dict[str, np.ndarray] = {}
    for i, name in enumerate(series_names_in):
        if quantile_list is None:
            # Point forecast: shape (steps, 1)
            predictions[name] = np.asarray(point_forecast[i]).reshape(-1, 1)
        else:
            q_indices = [round(q * 10) for q in quantile_list]
            qf = np.asarray(quantile_forecast[i])
            predictions[name] = qf[:, q_indices]  # (steps, n_quantiles)

    return predictions

_load_model 

_load_model()

Load (but do not compile) the TimesFM model into self._model if not already set.

Returns:

Type Description
None

Raises:

Type Description
ImportError

If timesfm[torch] is not installed.
Notes

The model is imported lazily from timesfm and loaded via TimesFM_2p5_200M_torch.from_pretrained. Compilation is deferred to _ensure_compiled, which is called from predict with the actual forecast horizon so that the compiled decode graph is sized exactly for the requested number of steps rather than the (much larger) max_horizon ceiling. This method is a no-op when self._model is already populated.

Source code in skforecast/foundation/_adapters.py 
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
def _load_model(self) -> None:
    """
    Load (but do not compile) the TimesFM model into `self._model`
    if not already set.

    Returns
    -------
    None

    Raises
    ------
    ImportError
        If `timesfm[torch]` is not installed.

    Notes
    -----
    The model is imported lazily from `timesfm` and loaded via
    `TimesFM_2p5_200M_torch.from_pretrained`. Compilation is deferred to
    `_ensure_compiled`, which is called from `predict` with the actual
    forecast horizon so that the compiled decode graph is sized exactly
    for the requested number of steps rather than the (much larger)
    `max_horizon` ceiling. This method is a no-op when `self._model` is
    already populated.
    """

    if self._model is not None:
        return
    try:
        import timesfm
    except ImportError as exc:
        raise ImportError(
            "timesfm is required for TimesFMAdapter. "
            "Install it with `pip install timesfm`."
        ) from exc

    # Workaround for a compatibility issue between huggingface_hub and
    # timesfm: huggingface_hub's `from_pretrained` passes `proxies` and
    # `resume_download` to `_from_pretrained`, but timesfm's
    # `_from_pretrained` does not declare them as explicit parameters, so
    # they fall into **model_kwargs and are forwarded to __init__, raising
    # a TypeError. A local subclass overrides `_from_pretrained` to absorb
    # those kwargs without modifying any global state.
    class _TimesFMCompat(timesfm.TimesFM_2p5_200M_torch):
        @classmethod
        def _from_pretrained(cls, *, proxies=None, resume_download=None, **kwargs):  # type: ignore[override]
            return super()._from_pretrained(**kwargs)

    self._model = _TimesFMCompat.from_pretrained(self.model_id)

_ensure_compiled 

_ensure_compiled(steps)

Compile the model for the given forecast horizon if not already compiled for at least steps steps.

Parameters:

Name Type Description Default
steps int

The forecast horizon that the model must support.

 required

Returns:

Type Description
None
Notes

This is separated from _load_model so that compilation uses the actual number of requested forecast steps rather than max_horizon. TimesFM's compiled decode always runs forecast_config.max_horizon autoregressive decode iterations regardless of the requested horizon; the true horizon is only used to slice the output afterwards. When the compiled max_horizon is large (e.g. the default 512) but steps is small (e.g. 12), the model performs up to (max_horizon - 1) // output_patch_len unnecessary extra transformer forward passes per inference call. Compiling here with max_horizon = steps reduces those wasted passes to zero for the typical backtesting case where steps is constant across folds.

If the model was already compiled for a horizon >= steps (e.g. a pre-compiled model passed via the model constructor argument), this method is a no-op.

Source code in skforecast/foundation/_adapters.py 
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
def _ensure_compiled(self, steps: int) -> None:
    """
    Compile the model for the given forecast horizon if not already
    compiled for at least `steps` steps.

    Parameters
    ----------
    steps : int
        The forecast horizon that the model must support.

    Returns
    -------
    None

    Notes
    -----
    This is separated from `_load_model` so that compilation uses the
    *actual* number of requested forecast steps rather than `max_horizon`.
    TimesFM's compiled decode always runs `forecast_config.max_horizon`
    autoregressive decode iterations regardless of the requested horizon;
    the true horizon is only used to *slice* the output afterwards. When
    the compiled `max_horizon` is large (e.g. the default 512) but
    `steps` is small (e.g. 12), the model performs up to
    `(max_horizon - 1) // output_patch_len` unnecessary extra transformer
    forward passes per inference call. Compiling here with
    `max_horizon = steps` reduces those wasted passes to zero for the
    typical backtesting case where `steps` is constant across folds.

    If the model was already compiled for a horizon `>= steps` (e.g. a
    pre-compiled model passed via the `model` constructor argument), this
    method is a no-op.
    """

    fc = getattr(self._model, 'forecast_config', None)
    if fc is not None and steps <= fc.max_horizon:
        return

    import timesfm
    self._model.compile(
        timesfm.ForecastConfig(
            max_context = self.context_length,
            max_horizon = steps,
            **self.forecast_config_kwargs,
        )
    )

skforecast.foundation._adapters.MoiraiAdapter 

MoiraiAdapter(
    model_id,
    *,
    module=None,
    context_length=2048,
    device="auto"
)

Adapter for Salesforce Moirai foundation models.

Parameters:

Name Type Description Default
model_id str

HuggingFace model ID, e.g. "Salesforce/moirai-2.0-R-small". Must be a Salesforce/moirai-2.0-R-{small,base,large} variant.

 required
module object

Pre-loaded Moirai2Module instance. If None, the module is loaded lazily on the first call to predict.

 None
context_length int

Maximum number of historical observations to use as context. At fit time only the last context_length observations are stored. At predict time, if context is longer than context_length it is trimmed to this length; if it is shorter, all available observations are used as-is. Must be a positive integer.

 2048
device str

Device placement for the model. "auto" selects the best available accelerator (CUDA > MPS > CPU). Also accepts explicit values such as "cuda", "mps", or "cpu".

 'auto'

Attributes:

Name Type Description
model_id str

HuggingFace model ID.
context_ dict

Stored training series after fitting.
context_exog_ dict

Not used, present here for API consistency by convention.
context_length int

Maximum number of historical observations used as context.
device str

Device placement for the model.
_forecast_obj object

Internal Moirai forecast object, populated at the first call to predict.
is_fitted bool

Whether the adapter has been fitted.
Notes

Moirai supports only the fixed quantile levels [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]. Requesting any other level raises a ValueError.

Covariate support via the high-level Moirai2Forecast.predict() API is not functional: the padding/truncation loop inside predict() clips every list-valued field — including feat_dynamic_real — to context_length, discarding the future portion that future covariates require. Passing exog or context_exog issues an IgnoredArgumentWarning and the values are discarded.

References

.. [1] https://github.com/SalesforceAIResearch/uni2ts

.. [2] https://huggingface.co/Salesforce/moirai-2.0-R-small

Initialise the adapter.

Parameters:

Name Type Description Default
model_id str

HuggingFace model ID, e.g. "Salesforce/moirai-2.0-R-small".

 required
module object

Pre-loaded Moirai2Module instance. If None, the module is loaded lazily on the first call to predict.

 None
context_length int

Maximum number of historical observations to retain as context. At fit time only the last context_length observations of series are stored. At predict time, if context is longer than context_length it is trimmed to this length; if it is shorter, all available observations are passed as-is. Must be a positive integer.

 2048
device str

Device placement for the model. "auto" selects the best available accelerator (CUDA > MPS > CPU). Also accepts explicit values such as "cuda", "mps", or "cpu".

 'auto'

Methods:

Name Description
get_params

Return the adapter's constructor parameters.
set_params

Set adapter parameters. Resets the module and forecast object when
fit

Store the training series.
predict

Generate predictions using Moirai.
Source code in skforecast/foundation/_adapters.py 
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
def __init__(
    self,
    model_id: str,
    *,
    module: Any | None = None,
    context_length: int = 2048,
    device: str = "auto",
) -> None:
    """
    Initialise the adapter.

    Parameters
    ----------
    model_id : str
        HuggingFace model ID, e.g. `"Salesforce/moirai-2.0-R-small"`.
    module : object, default None
        Pre-loaded `Moirai2Module` instance. If `None`, the module
        is loaded lazily on the first call to `predict`.
    context_length : int, default 2048
        Maximum number of historical observations to retain as context.
        At `fit` time only the last `context_length` observations of
        `series` are stored. At `predict` time, if `context`
        is longer than `context_length` it is trimmed to this length;
        if it is shorter, all available observations are passed as-is.
        Must be a positive integer.
    device : str, default 'auto'
        Device placement for the model. `"auto"` selects the best
        available accelerator (CUDA > MPS > CPU). Also accepts
        explicit values such as `"cuda"`, `"mps"`, or `"cpu"`.

    """

    _validate_positive_int("context_length", context_length)

    self.model_id       = model_id
    self._module        = module
    self.context_       = None
    self.context_exog_  = None
    self.context_length = context_length
    self.device         = device
    self._forecast_obj  = None
    self.is_fitted      = False

Attributes

SUPPORTED_QUANTILES class-attribute instance-attribute 

SUPPORTED_QUANTILES = [
    0.1,
    0.2,
    0.3,
    0.4,
    0.5,
    0.6,
    0.7,
    0.8,
    0.9,
]

allow_exog class-attribute instance-attribute 

allow_exog = False

model_id instance-attribute 

model_id = model_id

_module instance-attribute 

_module = module

context_ instance-attribute 

context_ = None

context_exog_ instance-attribute 

context_exog_ = None

context_length instance-attribute 

context_length = context_length

device instance-attribute 

device = device

_forecast_obj instance-attribute 

_forecast_obj = None

is_fitted instance-attribute 

is_fitted = False

Methods:

get_params 

get_params()

Return the adapter's constructor parameters.

Returns:

Name Type Description
params dict

Keys: model_id, context_length, device.
Source code in skforecast/foundation/_adapters.py 
 991
 992
 993
 994
 995
 996
 997
 998
 999
1000
1001
1002
1003
1004
def get_params(self) -> dict:
    """
    Return the adapter's constructor parameters.

    Returns
    -------
    params : dict
        Keys: `model_id`, `context_length`, `device`.
    """
    return {
        'model_id':       self.model_id,
        'context_length': self.context_length,
        'device':         self.device,
    }

set_params 

set_params(**params)

Set adapter parameters. Resets the module and forecast object when model_id, context_length, or device changes.

Parameters:

Name Type Description Default
**params

Valid keys: model_id, context_length, device.

 {}

Returns:

Name Type Description
self MoiraiAdapter
Source code in skforecast/foundation/_adapters.py 
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
def set_params(self, **params) -> MoiraiAdapter:
    """
    Set adapter parameters. Resets the module and forecast object when
    `model_id`, `context_length`, or `device` changes.

    Parameters
    ----------
    **params :
        Valid keys: `model_id`, `context_length`, `device`.

    Returns
    -------
    self : MoiraiAdapter

    """

    def validate(p: dict) -> dict:
        if "context_length" in p:
            _validate_positive_int("context_length", p["context_length"])
        return p

    def _reset_module() -> None:
        self._module = None
        self._forecast_obj = None

    return _apply_set_params(
        self, params,
        validate=validate,
        resets=(
            ({"model_id", "context_length", "device"}, _reset_module),
        ),
    )

fit 

fit(context, context_exog)

Store the training series. No model training occurs since Moirai is a zero-shot inference model.

All input normalization and validation is performed upstream by FoundationModel; this method receives canonical dicts only.

Parameters:

Name Type Description Default
context dict pandas Series

Normalized training series, one entry per series.

 required
context_exog Any

Not used, present here for API consistency by convention.

 required

Returns:

Name Type Description
self MoiraiAdapter
Source code in skforecast/foundation/_adapters.py 
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
1067
def fit(
    self,
    context: dict[str, pd.Series],
    context_exog: Any,
) -> MoiraiAdapter:
    """
    Store the training series.
    No model training occurs since Moirai is a zero-shot inference model.

    All input normalization and validation is performed upstream by
    `FoundationModel`; this method receives canonical dicts only.

    Parameters
    ----------
    context : dict pandas Series
        Normalized training series, one entry per series.
    context_exog : Any
        Not used, present here for API consistency by convention.

    Returns
    -------
    self : MoiraiAdapter

    """

    self.context_ = context
    self.is_fitted = True

    return self

predict 

predict(steps, context, context_exog, exog, quantiles)

Generate predictions using Moirai.

All input normalization, validation, and context trimming is performed upstream by FoundationModel; this method receives pre-processed dicts only.

Parameters:

Name Type Description Default
steps int

Number of steps ahead to forecast.

 required
context dict pandas Series

Per-series context windows (already trimmed to context_length).

 required
context_exog Any

Not used, present here for API consistency by convention.

 required
exog Any

Not used, present here for API consistency by convention.

 required
quantiles list of float or None

Quantile levels. Must be a subset of SUPPORTED_QUANTILES.

 required

Returns:

Name Type Description
predictions dict

Keys are series names. Each value is a 2-D array of shape (steps, n_quantiles).

Raises:

Type Description
ValueError

If a requested quantile level is not in SUPPORTED_QUANTILES.
Source code in skforecast/foundation/_adapters.py 
1069
1070
1071
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
def predict(
    self,
    steps: int,
    context: dict[str, pd.Series],
    context_exog: Any,
    exog: Any,
    quantiles: list[float] | tuple[float] | None,
) -> dict[str, np.ndarray]:
    """
    Generate predictions using Moirai.

    All input normalization, validation, and context trimming is
    performed upstream by `FoundationModel`; this method receives
    pre-processed dicts only.

    Parameters
    ----------
    steps : int
        Number of steps ahead to forecast.
    context : dict pandas Series
        Per-series context windows (already trimmed to
        `context_length`).
    context_exog : Any
        Not used, present here for API consistency by convention.
    exog : Any
        Not used, present here for API consistency by convention.
    quantiles : list of float or None
        Quantile levels. Must be a subset of `SUPPORTED_QUANTILES`.

    Returns
    -------
    predictions : dict
        Keys are series names. Each value is a 2-D array of shape
        `(steps, n_quantiles)`.

    Raises
    ------
    ValueError
        If a requested quantile level is not in `SUPPORTED_QUANTILES`.

    """

    if quantiles is not None:
        quantile_list = list(quantiles)
        for q in quantile_list:
            if not any(abs(q - sq) < 1e-9 for sq in self.SUPPORTED_QUANTILES):
                raise ValueError(
                    f"Moirai only supports quantile levels "
                    f"{self.SUPPORTED_QUANTILES}. Got {q!r}. "
                    f"Quantile interpolation is not supported."
                )
    else:
        quantile_list = None

    quantile_levels = quantile_list if quantile_list is not None else [0.5]
    q_indices = [
        next(
            i for i, sq in enumerate(self.SUPPORTED_QUANTILES)
            if abs(q - sq) < 1e-9
        )
        for q in quantile_levels
    ]

    series_names_in = list(context.keys())
    inputs_list = [
        context[name].to_numpy(dtype=np.float32).reshape(-1, 1)
        for name in series_names_in
    ]

    raw = self._run_inference(inputs_list, steps)

    predictions: dict[str, np.ndarray] = {}
    for i, name in enumerate(series_names_in):
        predictions[name] = raw[i][q_indices, :].T  # (steps, n_quantiles)

    return predictions

_load_module 

_load_module()

Load the Moirai2Module into self._module if not already set.

Returns:

Type Description
None

Raises:

Type Description
ImportError

If uni2ts is not installed.
Notes

The module is imported lazily from uni2ts and instantiated via Moirai2Module.from_pretrained, then set to evaluation mode. This method is a no-op when self._module is already populated.

Source code in skforecast/foundation/_adapters.py 
1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
def _load_module(self) -> None:
    """
    Load the `Moirai2Module` into `self._module` if not already set.

    Returns
    -------
    None

    Raises
    ------
    ImportError
        If `uni2ts` is not installed.

    Notes
    -----
    The module is imported lazily from `uni2ts` and instantiated via
    `Moirai2Module.from_pretrained`, then set to evaluation mode.
    This method is a no-op when `self._module` is already populated.
    """

    if self._module is not None:
        return
    try:
        from uni2ts.model.moirai2 import Moirai2Module
    except ImportError as exc:
        raise ImportError(
            "uni2ts is required for MoiraiAdapter. "
            "Install it with `pip install uni2ts`."
        ) from exc
    self._module = Moirai2Module.from_pretrained(self.model_id)
    self._module.eval()

_ensure_forecast_obj 

_ensure_forecast_obj()

Build the Moirai2Forecast inference wrapper if not already set.

Returns:

Type Description
None

Raises:

Type Description
ImportError

If uni2ts is not installed.
Notes

Calls _load_module then wraps self._module in a Moirai2Forecast with prediction_length=1 (overridden per-call via hparams_context), sets it to evaluation mode, and moves it to the device specified by self.device. This method is a no-op when self._forecast_obj is already populated.

Source code in skforecast/foundation/_adapters.py 
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
def _ensure_forecast_obj(self) -> None:
    """
    Build the `Moirai2Forecast` inference wrapper if not already set.

    Returns
    -------
    None

    Raises
    ------
    ImportError
        If `uni2ts` is not installed.

    Notes
    -----
    Calls `_load_module` then wraps `self._module` in a
    `Moirai2Forecast` with `prediction_length=1` (overridden
    per-call via `hparams_context`), sets it to evaluation mode,
    and moves it to the device specified by `self.device`.
    This method is a no-op when `self._forecast_obj` is already
    populated.
    """

    if self._forecast_obj is not None:
        return

    self._load_module()
    from uni2ts.model.moirai2 import Moirai2Forecast

    self._forecast_obj = Moirai2Forecast(
        module                     = self._module,
        prediction_length          = 1,
        context_length             = self.context_length,
        target_dim                 = 1,
        feat_dynamic_real_dim      = 0,
        past_feat_dynamic_real_dim = 0,
    ).eval()

    resolved_device = _resolve_torch_device(self.device)
    if resolved_device == "mps":
        warnings.warn(
            "MPS device is not supported by Moirai because the uni2ts "
            "library uses float64 operations internally. Falling back "
            "to CPU.",
            stacklevel=6,
        )
        resolved_device = "cpu"
    self._forecast_obj.to(resolved_device)

_run_inference 

_run_inference(inputs_list, steps)

Run batched inference with Moirai2Forecast.

Parameters:

Name Type Description Default
inputs_list list of numpy ndarray

List of 2-D arrays with shape (T, 1), one per series. Each array holds float32 values.

 required
steps int

Forecast horizon.

 required

Returns:

Name Type Description
raw numpy ndarray

Array of shape (n_series, 9, steps) containing quantile forecasts for the 9 fixed levels in SUPPORTED_QUANTILES order.
Source code in skforecast/foundation/_adapters.py 
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
def _run_inference(
    self,
    inputs_list: list[np.ndarray],
    steps: int,
) -> np.ndarray:
    """
    Run batched inference with `Moirai2Forecast`.

    Parameters
    ----------
    inputs_list : list of numpy ndarray
        List of 2-D arrays with shape `(T, 1)`, one per series.
        Each array holds `float32` values.
    steps : int
        Forecast horizon.

    Returns
    -------
    raw : numpy ndarray
        Array of shape `(n_series, 9, steps)` containing quantile
        forecasts for the 9 fixed levels in `SUPPORTED_QUANTILES`
        order.

    """

    self._ensure_forecast_obj()
    with self._forecast_obj.hparams_context(prediction_length=steps):
        raw = self._forecast_obj.predict(inputs_list)

    return raw

skforecast.foundation._adapters.TabICLAdapter 

TabICLAdapter(
    model_id,
    *,
    model=None,
    context_length=4096,
    point_estimate="mean",
    tabicl_config=None,
    temporal_features=None,
    show_progress=False
)

Adapter for TabICL zero-shot time-series foundation models.

Parameters:

Name Type Description Default
model_id str

HuggingFace model ID, e.g. "soda-inria/tabicl".

 required
model object

Pre-instantiated TabICLForecaster instance. If None, a new instance is created lazily on the first call to predict. Intended for testing only.

 None
context_length int

Maximum number of historical observations to use as context. At fit time only the last context_length observations are stored. At predict time, if context is longer than context_length it is trimmed to this length; if it is shorter, all available observations are used as-is. Must be a positive integer.

 4096
point_estimate str

Method used to derive the point forecast from the TabICL output. Accepted values: 'mean', 'median'.

 'mean'
tabicl_config dict

Additional keyword arguments forwarded verbatim to TabICLRegressor at inference time. If None, defaults to empty dict (TabICL's own defaults).

 None
temporal_features list

List of TimeTransform instances applied to the time series before inference. If None, TabICL uses its default transforms: [IndexEncoder(), DatetimeEncoder(), AutoPeriodicEncoder()]. Pass an empty list to disable all temporal feature engineering.

 None
show_progress bool

If False, the tqdm progress bar emitted by the underlying TabICL dispatch loop (GPU 0: ...) is suppressed.

 False

Attributes:

Name Type Description
model_id str

HuggingFace model ID.
context_ dict

Stored training series after fitting.
context_exog_ dict

Stored historical exogenous variables after fitting.
context_length int

Maximum number of historical observations used as context.
point_estimate str

Point forecast method.
tabicl_config dict

Additional configuration forwarded to TabICLRegressor.
temporal_features list

Temporal feature transforms applied to the series.
show_progress bool

Whether the TabICL dispatch progress bar is shown.
is_fitted bool

Whether the adapter has been fitted.
_model object

Internal TabICLForecaster instance. None until the first call to predict, after which it is cached for reuse.
Notes

TabICL supports arbitrary quantile levels (any float in [0, 1]), unlike models with fixed quantile sets such as TimesFM or Moirai.

Covariate support is available: extra columns in context and exog are forwarded as covariates. TabICL uses only the intersection of columns present in both context and future data (missing values are filled with NaN).

Series with a RangeIndex are accepted. Internally, TabICL requires datetime timestamps, so a synthetic daily DatetimeIndex (starting 2000-01-01) is used. Calendar-based transforms (DatetimeEncoder, AutoPeriodicEncoder) will not be meaningful for such series; consider passing temporal_features=[] or temporal_features=[IndexEncoder()] in that case.

References

.. [1] https://github.com/soda-inria/tabicl

.. [2] https://tabicl.readthedocs.io/en/latest/

Initialise the adapter.

Parameters:

Name Type Description Default
model_id str

HuggingFace model ID, e.g. "soda-inria/tabicl".

 required
model object

Pre-instantiated TabICLForecaster instance. If None, a new instance is created lazily on the first call to predict. Intended for testing only.

 None
context_length int

Maximum number of historical observations to retain as context. At fit time only the last context_length observations of series (and exog) are stored. At predict time, if context is longer than context_length it is trimmed to this length before inference; if it is shorter, all available observations are passed as-is. Must be a positive integer.

 4096
point_estimate str

Method used to derive the point forecast. Accepted values: 'mean', 'median'.

 'mean'
tabicl_config dict

Additional keyword arguments forwarded verbatim to TabICLRegressor at inference time.

 None
temporal_features list

List of TimeTransform instances applied before inference. If None, TabICL uses its defaults. Pass [] to disable all temporal feature engineering.

 None
show_progress bool

If False, the tqdm progress bar emitted by the underlying TabICL dispatch loop (GPU 0: ...) is suppressed by redirecting stderr during the predict_df call.

 False

Methods:

Name Description
get_params

Return the adapter's constructor parameters.
set_params

Set adapter parameters. Resets the model when a parameter that affects
fit

Store the training series and optional historical exogenous variables.
predict

Generate predictions using TabICL.
Source code in skforecast/foundation/_adapters.py 
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404
1405
def __init__(
    self,
    model_id: str,
    *,
    model: Any | None = None,
    context_length: int = 4096,
    point_estimate: str = "mean",
    tabicl_config: dict[str, Any] | None = None,
    temporal_features: list[Any] | None = None,
    show_progress: bool = False,
) -> None:
    """
    Initialise the adapter.

    Parameters
    ----------
    model_id : str
        HuggingFace model ID, e.g. `"soda-inria/tabicl"`.
    model : object, default None
        Pre-instantiated `TabICLForecaster` instance. If `None`, a new
        instance is created lazily on the first call to `predict`.
        Intended for testing only.
    context_length : int, default 4096
        Maximum number of historical observations to retain as context.
        At `fit` time only the last `context_length` observations of
        `series` (and `exog`) are stored. At `predict` time, if
        `context` is longer than `context_length` it is trimmed to
        this length before inference; if it is shorter, all available
        observations are passed as-is. Must be a positive integer.
    point_estimate : str, default 'mean'
        Method used to derive the point forecast. Accepted values:
        `'mean'`, `'median'`.
    tabicl_config : dict, default None
        Additional keyword arguments forwarded verbatim to
        `TabICLRegressor` at inference time.
    temporal_features : list, default None
        List of `TimeTransform` instances applied before inference. If
        `None`, TabICL uses its defaults. Pass `[]` to disable all
        temporal feature engineering.
    show_progress : bool, default False
        If `False`, the tqdm progress bar emitted by the underlying
        TabICL dispatch loop (`GPU 0: ...`) is suppressed by
        redirecting stderr during the `predict_df` call.

    """

    _validate_positive_int("context_length", context_length)
    if point_estimate not in ("mean", "median"):
        raise ValueError(
            f"`point_estimate` must be 'mean' or 'median'. Got {point_estimate!r}."
        )

    self.model_id          = model_id
    self._model            = model
    self.context_          = None
    self.context_exog_     = None
    self.context_length    = context_length
    self.point_estimate    = point_estimate
    self.tabicl_config     = tabicl_config or {}
    self.temporal_features = temporal_features
    self.show_progress     = show_progress
    self.is_fitted         = False

Attributes

allow_exog class-attribute instance-attribute 

allow_exog = True

model_id instance-attribute 

model_id = model_id

_model instance-attribute 

_model = model

context_ instance-attribute 

context_ = None

context_exog_ instance-attribute 

context_exog_ = None

context_length instance-attribute 

context_length = context_length

point_estimate instance-attribute 

point_estimate = point_estimate

tabicl_config instance-attribute 

tabicl_config = tabicl_config or {}

temporal_features instance-attribute 

temporal_features = temporal_features

show_progress instance-attribute 

show_progress = show_progress

is_fitted instance-attribute 

is_fitted = False

Methods:

get_params 

get_params()

Return the adapter's constructor parameters.

Returns:

Name Type Description
params dict

Keys: model_id, context_length, point_estimate, tabicl_config, temporal_features, show_progress. tabicl_config is returned as None when no additional config was set (i.e. when the internal dict is empty).
Source code in skforecast/foundation/_adapters.py 
1407
1408
1409
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
def get_params(self) -> dict:
    """
    Return the adapter's constructor parameters.

    Returns
    -------
    params : dict
        Keys: `model_id`, `context_length`, `point_estimate`,
        `tabicl_config`, `temporal_features`, `show_progress`.
        `tabicl_config` is returned as `None` when no additional
        config was set (i.e. when the internal dict is empty).

    """
    return {
        "model_id":          self.model_id,
        "context_length":    self.context_length,
        "point_estimate":    self.point_estimate,
        "tabicl_config":     self.tabicl_config or None,
        "temporal_features": self.temporal_features,
        "show_progress":     self.show_progress,
    }

set_params 

set_params(**params)

Set adapter parameters. Resets the model when a parameter that affects the TabICLForecaster instance changes; toggling show_progress does not reset the model.

Parameters:

Name Type Description Default
**params

Valid keys: model_id, context_length, point_estimate, tabicl_config, temporal_features, show_progress.

 {}

Returns:

Name Type Description
self TabICLAdapter
Source code in skforecast/foundation/_adapters.py 
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
def set_params(self, **params) -> TabICLAdapter:
    """
    Set adapter parameters. Resets the model when a parameter that affects
    the `TabICLForecaster` instance changes; toggling `show_progress` does
    not reset the model.

    Parameters
    ----------
    **params :
        Valid keys: `model_id`, `context_length`, `point_estimate`,
        `tabicl_config`, `temporal_features`, `show_progress`.

    Returns
    -------
    self : TabICLAdapter

    """

    def validate(p: dict) -> dict:
        if "context_length" in p:
            _validate_positive_int("context_length", p["context_length"])
        if "point_estimate" in p and p["point_estimate"] not in ("mean", "median"):
            raise ValueError(
                f"`point_estimate` must be 'mean' or 'median'. "
                f"Got {p['point_estimate']!r}."
            )
        if "tabicl_config" in p:
            p["tabicl_config"] = p["tabicl_config"] or {}
        if "show_progress" in p and not isinstance(p["show_progress"], bool):
            raise ValueError(
                f"`show_progress` must be a bool. Got {p['show_progress']!r}."
            )
        return p

    return _apply_set_params(
        self, params,
        validate=validate,
        resets=(
            (
                {"model_id", "context_length", "point_estimate",
                 "tabicl_config", "temporal_features"},
                lambda: setattr(self, "_model", None),
            ),
        ),
    )

fit 

fit(context, context_exog)

Store the training series and optional historical exogenous variables. No model training occurs since TabICL is a zero-shot inference model.

All input normalization and validation is performed upstream by FoundationModel; this method receives canonical dicts only.

Parameters:

Name Type Description Default
context dict pandas Series

Normalized training series, one entry per series.

 required
context_exog dict pandas DataFrame, pandas Series, or None

Per-series historical exogenous variables (past covariates).

 required

Returns:

Name Type Description
self TabICLAdapter
Source code in skforecast/foundation/_adapters.py 
1475
1476
1477
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
1503
1504
def fit(
    self,
    context: dict[str, pd.Series],
    context_exog: dict[str, pd.DataFrame | pd.Series | None] | None,
) -> TabICLAdapter:
    """
    Store the training series and optional historical exogenous variables.
    No model training occurs since TabICL is a zero-shot inference model.

    All input normalization and validation is performed upstream by
    `FoundationModel`; this method receives canonical dicts only.

    Parameters
    ----------
    context : dict pandas Series
        Normalized training series, one entry per series.
    context_exog : dict pandas DataFrame, pandas Series, or None
        Per-series historical exogenous variables (past covariates).

    Returns
    -------
    self : TabICLAdapter

    """

    self.context_      = context
    self.context_exog_ = context_exog
    self.is_fitted     = True

    return self

predict 

predict(steps, context, context_exog, exog, quantiles)

Generate predictions using TabICL.

All input normalization, validation, and context trimming is performed upstream by FoundationModel; this method receives pre-processed dicts only.

Parameters:

Name Type Description Default
steps int

Number of steps ahead to forecast.

 required
context dict pandas Series

Per-series context windows (already trimmed to context_length).

 required
context_exog dict pandas DataFrame, pandas Series, or None

Per-series past covariates (already trimmed).

 required
exog dict pandas DataFrame, pandas Series, or None

Per-series future covariates for the forecast horizon.

 required
quantiles list of float or None

Quantile levels to return. If None, a point forecast is produced (shape (steps, 1)). Accepts any float in [0, 1].

 required

Returns:

Name Type Description
predictions dict

Keys are series names. Each value is a 2-D numpy ndarray of shape (steps, n_quantiles).
Source code in skforecast/foundation/_adapters.py 
1506
1507
1508
1509
1510
1511
1512
1513
1514
1515
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
def predict(
    self,
    steps: int,
    context: dict[str, pd.Series],
    context_exog: dict[str, pd.DataFrame | pd.Series | None] | None,
    exog: dict[str, pd.DataFrame | pd.Series | None] | None,
    quantiles: list[float] | tuple[float] | None,
) -> dict[str, np.ndarray]:
    """
    Generate predictions using TabICL.

    All input normalization, validation, and context trimming is
    performed upstream by `FoundationModel`; this method receives
    pre-processed dicts only.

    Parameters
    ----------
    steps : int
        Number of steps ahead to forecast.
    context : dict pandas Series
        Per-series context windows (already trimmed to
        `context_length`).
    context_exog : dict pandas DataFrame, pandas Series, or None
        Per-series past covariates (already trimmed).
    exog : dict pandas DataFrame, pandas Series, or None
        Per-series future covariates for the forecast horizon.
    quantiles : list of float or None
        Quantile levels to return. If `None`, a point forecast is
        produced (shape `(steps, 1)`). Accepts any float in `[0, 1]`.

    Returns
    -------
    predictions : dict
        Keys are series names. Each value is a 2-D numpy ndarray of
        shape `(steps, n_quantiles)`.

    """

    self._load_model()

    quantile_list = list(quantiles) if quantiles is not None else None
    tabicl_quantiles = (
        quantile_list
        if quantile_list is not None
        else [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
    )

    series_names_in = list(context.keys())

    first_series = next(iter(context.values()))
    is_datetime = isinstance(first_series.index, pd.DatetimeIndex)

    if not is_datetime:
        warnings.warn(
            "TabICLAdapter received series with a non-DatetimeIndex. "
            "TabICL requires datetime timestamps internally; a synthetic "
            "daily DatetimeIndex (starting 2000-01-01) will be used. "
            "Calendar-based temporal features (DatetimeEncoder, "
            "AutoPeriodicEncoder) will not be meaningful for "
            "integer-indexed data. Consider passing "
            "`temporal_features=[]` to disable calendar feature "
            "transforms.",
            # stacklevel=3: TabICLAdapter.predict → FoundationModel.predict → user
            stacklevel=3,
        )

    context_df = self._build_context_df(
                     series_names = series_names_in, 
                     context      = context, 
                     context_exog = context_exog, 
                     is_datetime  = is_datetime
                 )

    future_df = self._build_future_df(
                    series_names = series_names_in, 
                    context      = context, 
                    exog         = exog, 
                    steps        = steps, 
                    is_datetime  = is_datetime
                )

    _stderr_cm = (
        contextlib.redirect_stderr(io.StringIO())
        if not self.show_progress
        else contextlib.nullcontext()
    )
    with _stderr_cm:
        result_df = self._model.predict_df(
                        context_df = context_df,
                        future_df  = future_df,
                        quantiles  = tabicl_quantiles,
                    )

    # result_df is a plain DataFrame with MultiIndex (item_id, timestamp).
    # columns: "target" (str) and quantile levels as float column names.
    predictions: dict[str, np.ndarray] = {}
    for name in series_names_in:
        group = result_df.loc[name]  # DataFrame indexed by timestamp
        if quantile_list is None:
            predictions[name] = group["target"].to_numpy().reshape(-1, 1)
        else:
            predictions[name] = group[quantile_list].to_numpy()

    return predictions

_load_model 

_load_model()

Load the TabICLForecaster into self._model if not already set.

Returns:

Type Description
None

Raises:

Type Description
ImportError

If tabicl[forecast] is not installed.
Notes

The model is imported lazily from tabicl and instantiated with the current adapter parameters. This method is a no-op when self._model is already populated (either by a prior call or by the model test-injection parameter).

Source code in skforecast/foundation/_adapters.py 
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
def _load_model(self) -> None:
    """
    Load the `TabICLForecaster` into `self._model` if not already set.

    Returns
    -------
    None

    Raises
    ------
    ImportError
        If `tabicl[forecast]` is not installed.

    Notes
    -----
    The model is imported lazily from `tabicl` and instantiated with
    the current adapter parameters. This method is a no-op when
    `self._model` is already populated (either by a prior call or by
    the `model` test-injection parameter).
    """

    if self._model is not None:
        return
    try:
        from tabicl.forecast import TabICLForecaster
    except ImportError as exc:
        raise ImportError(
            "tabicl[forecast] is required for TabICLAdapter. "
            "Install it with `pip install tabicl[forecast]`."
        ) from exc

    self._model = TabICLForecaster(
                      max_context_length = self.context_length,
                      temporal_features  = self.temporal_features,
                      point_estimate     = self.point_estimate,
                      tabicl_config      = self.tabicl_config or {},
                  )

_get_timestamps 

_get_timestamps(series, is_datetime)

Return datetime timestamps for a context series.

For DatetimeIndex series the original index is returned. For RangeIndex series a synthetic daily DatetimeIndex starting at 2000-01-01 is created so that TabICL's requirement for datetime timestamps is satisfied.

Parameters:

Name Type Description Default
series pandas Series

The context series.

 required
is_datetime bool

Whether the series has a DatetimeIndex.

 required

Returns:

Name Type Description
timestamps pandas DatetimeIndex

Datetime timestamps aligned with the series values.
Source code in skforecast/foundation/_adapters.py 
1649
1650
1651
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
def _get_timestamps(
    self, series: pd.Series, is_datetime: bool
) -> pd.DatetimeIndex:
    """
    Return datetime timestamps for a context series.

    For `DatetimeIndex` series the original index is returned. For
    `RangeIndex` series a synthetic daily `DatetimeIndex` starting at
    2000-01-01 is created so that TabICL's requirement for datetime
    timestamps is satisfied.

    Parameters
    ----------
    series : pandas Series
        The context series.
    is_datetime : bool
        Whether the series has a `DatetimeIndex`.

    Returns
    -------
    timestamps : pandas DatetimeIndex
        Datetime timestamps aligned with the series values.

    """

    if is_datetime:
        return series.index

    return pd.date_range("2000-01-01", periods=len(series), freq="D")

_get_future_timestamps 

_get_future_timestamps(series, steps, is_datetime)

Return datetime timestamps for the forecast horizon.

For DatetimeIndex series the horizon is appended at the inferred frequency. For RangeIndex series the synthetic daily timeline (2000-01-01 + len(context) days) is extended by steps days.

Parameters:

Name Type Description Default
series pandas Series

The context series (used to determine the end timestamp and frequency).

 required
steps int

Number of steps ahead.

 required
is_datetime bool

Whether the series has a DatetimeIndex.

 required

Returns:

Name Type Description
timestamps pandas DatetimeIndex

Datetime timestamps for the steps forecast steps.
Source code in skforecast/foundation/_adapters.py 
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
def _get_future_timestamps(
    self, series: pd.Series, steps: int, is_datetime: bool
) -> pd.DatetimeIndex:
    """
    Return datetime timestamps for the forecast horizon.

    For `DatetimeIndex` series the horizon is appended at the inferred
    frequency. For `RangeIndex` series the synthetic daily timeline
    (2000-01-01 + len(context) days) is extended by `steps` days.

    Parameters
    ----------
    series : pandas Series
        The context series (used to determine the end timestamp and
        frequency).
    steps : int
        Number of steps ahead.
    is_datetime : bool
        Whether the series has a `DatetimeIndex`.

    Returns
    -------
    timestamps : pandas DatetimeIndex
        Datetime timestamps for the `steps` forecast steps.

    """

    if is_datetime:
        timestamps = expand_index(series.index, steps=steps)
    else:
        n = len(series)
        timestamps = pd.date_range(
                         start   = pd.Timestamp("2000-01-01") + pd.Timedelta(days=n),
                         periods = steps,
                         freq    = "D",
                     )

    return timestamps

_build_context_df 

_build_context_df(
    series_names, context, context_exog, is_datetime
)

Build a long-format context DataFrame expected by TabICL.

Each series' observations become rows with item_id, timestamp, target, and optional exogenous covariate columns.

Parameters:

Name Type Description Default
series_names list

Ordered list of series names.

 required
context dict pandas Series

Per-series context windows.

 required
context_exog dict or None

Per-series historical exogenous variables.

 required
is_datetime bool

Whether the series have a DatetimeIndex.

 required

Returns:

Name Type Description
context_df pandas DataFrame

Long-format DataFrame with columns item_id, timestamp, target, and any exogenous columns.
Source code in skforecast/foundation/_adapters.py 
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
1742
1743
1744
1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
1767
1768
1769
1770
def _build_context_df(
    self,
    series_names: list,
    context: dict[str, pd.Series],
    context_exog: dict[str, pd.DataFrame | None] | None,
    is_datetime: bool,
) -> pd.DataFrame:
    """
    Build a long-format context DataFrame expected by TabICL.

    Each series' observations become rows with `item_id`, `timestamp`,
    `target`, and optional exogenous covariate columns.

    Parameters
    ----------
    series_names : list
        Ordered list of series names.
    context : dict pandas Series
        Per-series context windows.
    context_exog : dict or None
        Per-series historical exogenous variables.
    is_datetime : bool
        Whether the series have a `DatetimeIndex`.

    Returns
    -------
    context_df : pandas DataFrame
        Long-format DataFrame with columns `item_id`, `timestamp`,
        `target`, and any exogenous columns.

    """

    context_df = []
    for name in series_names:
        series = context[name]
        n = len(series)
        part = pd.DataFrame({
            "item_id":   np.full(n, name),
            "timestamp": np.asarray(self._get_timestamps(series, is_datetime)),
            "target":    series.to_numpy(dtype=float),
        })
        exog_entry = (
            context_exog.get(name) if context_exog is not None else None
        )
        if exog_entry is not None:
            part = pd.concat(
                [part, exog_entry.reset_index(drop=True)], axis=1
            )
        context_df.append(part)

    context_df = pd.concat(context_df, ignore_index=True)

    return context_df

_build_future_df 

_build_future_df(
    series_names, context, exog, steps, is_datetime
)

Build a long-format future DataFrame expected by TabICL.

Each series' forecast horizon becomes rows with item_id, timestamp, and optional future exogenous covariate columns.

Parameters:

Name Type Description Default
series_names list

Ordered list of series names.

 required
context dict pandas Series

Per-series context windows (used to derive future timestamps).

 required
exog dict or None

Per-series future exogenous variables covering the forecast horizon.

 required
steps int

Number of steps ahead.

 required
is_datetime bool

Whether the series have a DatetimeIndex.

 required

Returns:

Name Type Description
future_df pandas DataFrame

Long-format DataFrame with columns item_id, timestamp, and any future exogenous columns.
Source code in skforecast/foundation/_adapters.py 
1772
1773
1774
1775
1776
1777
1778
1779
1780
1781
1782
1783
1784
1785
1786
1787
1788
1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
def _build_future_df(
    self,
    series_names: list,
    context: dict[str, pd.Series],
    exog: dict[str, pd.DataFrame | None] | None,
    steps: int,
    is_datetime: bool,
) -> pd.DataFrame:
    """
    Build a long-format future DataFrame expected by TabICL.

    Each series' forecast horizon becomes rows with `item_id`,
    `timestamp`, and optional future exogenous covariate columns.

    Parameters
    ----------
    series_names : list
        Ordered list of series names.
    context : dict pandas Series
        Per-series context windows (used to derive future timestamps).
    exog : dict or None
        Per-series future exogenous variables covering the forecast
        horizon.
    steps : int
        Number of steps ahead.
    is_datetime : bool
        Whether the series have a `DatetimeIndex`.

    Returns
    -------
    future_df : pandas DataFrame
        Long-format DataFrame with columns `item_id`, `timestamp`, and
        any future exogenous columns.

    """

    future_df = []
    for name in series_names:
        series = context[name]
        part = pd.DataFrame({
            "item_id":   np.full(steps, name),
            "timestamp": np.asarray(
                self._get_future_timestamps(series, steps, is_datetime)
            ),
        })
        future_exog = exog.get(name) if exog is not None else None
        if future_exog is not None:
            part = pd.concat(
                [part, future_exog.reset_index(drop=True)], axis=1
            )
        future_df.append(part)

    future_df = pd.concat(future_df, ignore_index=True)

    return future_df

skforecast.foundation._adapters.TabPFNAdapter 

TabPFNAdapter(
    model_id,
    *,
    model=None,
    context_length=32768,
    mode="local",
    point_estimate="median",
    tabpfn_model_config=None,
    temporal_features=None,
    show_progress=False
)

Adapter for Prior Labs TabPFN-TS zero-shot time-series foundation models.

TabPFN-TS frames forecasting as tabular regression: the series is featurized (running index, calendar features, automatically detected seasonal features) and a TabPFN regressor predicts the forecast horizon zero-shot.

Parameters:

Name Type Description Default
model_id str

Model ID, e.g. "priorlabs/tabpfn-ts". Used only to resolve this adapter; the underlying checkpoint is controlled by tabpfn_model_config (key model_path).

 required
model object

Pre-instantiated TabPFNTSPipeline instance. If None, a new instance is created lazily on the first call to predict. Intended for testing only.

 None
context_length int

Maximum number of historical observations to use as context. At fit time only the last context_length observations are stored. At predict time, if context is longer than context_length it is trimmed to this length; if it is shorter, all available observations are used as-is. Defaults to 32768, which matches the TabPFN-TS ship configuration; lower values (e.g. 4096) speed up inference at a small accuracy cost. Must be a positive integer.

 32768
mode str

Inference mode. 'local' runs the TabPFN model locally (CUDA > MPS > CPU selected automatically by the library; the checkpoint is downloaded on first use). 'client' sends the featurized data to the Prior Labs cloud API via tabpfn-client (no GPU needed, requires an account/API key).

 'local'
point_estimate str

Method used to aggregate the TabPFN ensemble output into the point forecast. Accepted values: 'mean', 'median', 'mode'.

 'median'
tabpfn_model_config dict

Additional configuration forwarded verbatim to the underlying TabPFN regressor (e.g. model_path, device). If None, the library defaults are used.

 None
temporal_features list

List of FeatureGenerator instances applied to the time series before inference. If None, TabPFN-TS uses its default transforms: [RunningIndexFeature(), CalendarFeature(), AutoSeasonalFeature()]. Pass an empty list to disable all temporal feature engineering.

 None
show_progress bool

If False, the tqdm progress bar emitted by the underlying TabPFN-TS dispatch loop (Predicting time series: ... on CPU, GPU 0: ... on GPU) is suppressed.

 False

Attributes:

Name Type Description
model_id str

Model ID.
context_ dict

Stored training series after fitting.
context_exog_ dict

Stored historical exogenous variables after fitting.
context_length int

Maximum number of historical observations used as context.
mode str

Inference mode, 'local' or 'client'.
point_estimate str

Point forecast aggregation method.
tabpfn_model_config dict

Additional configuration forwarded to the TabPFN regressor.
temporal_features list

Temporal feature transforms applied to the series.
show_progress bool

Whether the tqdm progress bar is shown during inference.
is_fitted bool

Whether the adapter has been fitted.
_model object

Internal TabPFNTSPipeline instance. None until the first call to predict, after which it is cached for reuse.
Notes

TabPFN-TS supports arbitrary quantile levels (any float in (0, 1)), unlike models with fixed quantile sets such as TimesFM or Moirai.

Covariate support is available for known-future covariates: extra columns present in both the historical context and the forecast horizon are used by the model. Covariates without future values are discarded by the library.

Series with a RangeIndex are accepted. Internally, TabPFN-TS requires datetime timestamps, so a synthetic daily DatetimeIndex (starting 2000-01-01) is used. Calendar-based transforms (CalendarFeature) will not be meaningful for such series; consider passing temporal_features=[] or [RunningIndexFeature()] in that case.

References

.. [1] https://github.com/PriorLabs/tabpfn-time-series

.. [2] https://priorlabs.ai/

Initialise the adapter.

Parameters:

Name Type Description Default
model_id str

Model ID, e.g. "priorlabs/tabpfn-ts".

 required
model object

Pre-instantiated TabPFNTSPipeline instance. If None, a new instance is created lazily on the first call to predict. Intended for testing only.

 None
context_length int

Maximum number of historical observations to retain as context. At fit time only the last context_length observations of series (and exog) are stored. At predict time, if context is longer than context_length it is trimmed to this length before inference; if it is shorter, all available observations are passed as-is. Must be a positive integer.

 32768
mode str

Inference mode. Accepted values: 'local', 'client'.

 'local'
point_estimate str

Method used to aggregate the TabPFN ensemble output into the point forecast. Accepted values: 'mean', 'median', 'mode'.

 'median'
tabpfn_model_config dict

Additional configuration forwarded verbatim to the underlying TabPFN regressor.

 None
temporal_features list

List of FeatureGenerator instances applied before inference. If None, TabPFN-TS uses its defaults. Pass [] to disable all temporal feature engineering.

 None
show_progress bool

If False, the tqdm progress bar emitted by the underlying TabPFN-TS dispatch loop (Predicting time series: ... on CPU, GPU 0: ... on GPU) is suppressed.

 False

Methods:

Name Description
get_params

Return the adapter's constructor parameters.
set_params

Set adapter parameters. Resets the model when a parameter that affects
fit

Store the training series and optional historical exogenous variables.
predict

Generate predictions using TabPFN-TS.
Source code in skforecast/foundation/_adapters.py 
1931
1932
1933
1934
1935
1936
1937
1938
1939
1940
1941
1942
1943
1944
1945
1946
1947
1948
1949
1950
1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
def __init__(
    self,
    model_id: str,
    *,
    model: Any | None = None,
    context_length: int = 32768,
    mode: str = "local",
    point_estimate: str = "median",
    tabpfn_model_config: dict[str, Any] | None = None,
    temporal_features: list[Any] | None = None,
    show_progress: bool = False,
) -> None:
    """
    Initialise the adapter.

    Parameters
    ----------
    model_id : str
        Model ID, e.g. `"priorlabs/tabpfn-ts"`.
    model : object, default None
        Pre-instantiated `TabPFNTSPipeline` instance. If `None`, a new
        instance is created lazily on the first call to `predict`.
        Intended for testing only.
    context_length : int, default 32768
        Maximum number of historical observations to retain as context.
        At `fit` time only the last `context_length` observations of
        `series` (and `exog`) are stored. At `predict` time, if
        `context` is longer than `context_length` it is trimmed to
        this length before inference; if it is shorter, all available
        observations are passed as-is. Must be a positive integer.
    mode : str, default 'local'
        Inference mode. Accepted values: `'local'`, `'client'`.
    point_estimate : str, default 'median'
        Method used to aggregate the TabPFN ensemble output into the
        point forecast. Accepted values: `'mean'`, `'median'`, `'mode'`.
    tabpfn_model_config : dict, default None
        Additional configuration forwarded verbatim to the underlying
        TabPFN regressor.
    temporal_features : list, default None
        List of `FeatureGenerator` instances applied before inference.
        If `None`, TabPFN-TS uses its defaults. Pass `[]` to disable all
        temporal feature engineering.
    show_progress : bool, default False
        If `False`, the tqdm progress bar emitted by the underlying
        TabPFN-TS dispatch loop (`Predicting time series: ...` on CPU,
        `GPU 0: ...` on GPU) is suppressed.

    """

    _validate_positive_int("context_length", context_length)
    if mode not in ("local", "client"):
        raise ValueError(
            f"`mode` must be 'local' or 'client'. Got {mode!r}."
        )
    if point_estimate not in ("mean", "median", "mode"):
        raise ValueError(
            f"`point_estimate` must be 'mean', 'median' or 'mode'. "
            f"Got {point_estimate!r}."
        )

    self.model_id            = model_id
    self._model              = model
    self.context_            = None
    self.context_exog_       = None
    self.context_length      = context_length
    self.mode                = mode
    self.point_estimate      = point_estimate
    self.tabpfn_model_config = tabpfn_model_config or {}
    self.temporal_features   = temporal_features
    self.show_progress       = show_progress
    self.is_fitted           = False

Attributes

allow_exog class-attribute instance-attribute 

allow_exog = True

model_id instance-attribute 

model_id = model_id

_model instance-attribute 

_model = model

context_ instance-attribute 

context_ = None

context_exog_ instance-attribute 

context_exog_ = None

context_length instance-attribute 

context_length = context_length

mode instance-attribute 

mode = mode

point_estimate instance-attribute 

point_estimate = point_estimate

tabpfn_model_config instance-attribute 

tabpfn_model_config = tabpfn_model_config or {}

temporal_features instance-attribute 

temporal_features = temporal_features

show_progress instance-attribute 

show_progress = show_progress

is_fitted instance-attribute 

is_fitted = False

Methods:

get_params 

get_params()

Return the adapter's constructor parameters.

Returns:

Name Type Description
params dict

Keys: model_id, context_length, mode, point_estimate, tabpfn_model_config, temporal_features, show_progress. tabpfn_model_config is returned as None when no additional config was set (i.e. when the internal dict is empty).
Source code in skforecast/foundation/_adapters.py 
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
def get_params(self) -> dict:
    """
    Return the adapter's constructor parameters.

    Returns
    -------
    params : dict
        Keys: `model_id`, `context_length`, `mode`, `point_estimate`,
        `tabpfn_model_config`, `temporal_features`, `show_progress`.
        `tabpfn_model_config` is returned as `None` when no additional
        config was set (i.e. when the internal dict is empty).

    """
    return {
        "model_id":            self.model_id,
        "context_length":      self.context_length,
        "mode":                self.mode,
        "point_estimate":      self.point_estimate,
        "tabpfn_model_config": self.tabpfn_model_config or None,
        "temporal_features":   self.temporal_features,
        "show_progress":       self.show_progress,
    }

set_params 

set_params(**params)

Set adapter parameters. Resets the model when a parameter that affects the TabPFNTSPipeline instance changes; toggling show_progress does not reset the model.

Parameters:

Name Type Description Default
**params

Valid keys: model_id, context_length, mode, point_estimate, tabpfn_model_config, temporal_features, show_progress.

 {}

Returns:

Name Type Description
self TabPFNAdapter
Source code in skforecast/foundation/_adapters.py 
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
2057
2058
2059
2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
def set_params(self, **params) -> TabPFNAdapter:
    """
    Set adapter parameters. Resets the model when a parameter that affects
    the `TabPFNTSPipeline` instance changes; toggling `show_progress` does
    not reset the model.

    Parameters
    ----------
    **params :
        Valid keys: `model_id`, `context_length`, `mode`,
        `point_estimate`, `tabpfn_model_config`, `temporal_features`,
        `show_progress`.

    Returns
    -------
    self : TabPFNAdapter

    """

    def validate(p: dict) -> dict:
        if "context_length" in p:
            _validate_positive_int("context_length", p["context_length"])
        if "mode" in p and p["mode"] not in ("local", "client"):
            raise ValueError(
                f"`mode` must be 'local' or 'client'. Got {p['mode']!r}."
            )
        if "point_estimate" in p and p["point_estimate"] not in ("mean", "median", "mode"):
            raise ValueError(
                f"`point_estimate` must be 'mean', 'median' or 'mode'. "
                f"Got {p['point_estimate']!r}."
            )
        if "tabpfn_model_config" in p:
            p["tabpfn_model_config"] = p["tabpfn_model_config"] or {}
        if "show_progress" in p and not isinstance(p["show_progress"], bool):
            raise ValueError(
                f"`show_progress` must be a bool. Got {p['show_progress']!r}."
            )
        return p

    return _apply_set_params(
        self, params,
        validate=validate,
        resets=(
            (
                {"model_id", "context_length", "mode", "point_estimate",
                 "tabpfn_model_config", "temporal_features"},
                lambda: setattr(self, "_model", None),
            ),
        ),
    )

fit 

fit(context, context_exog)

Store the training series and optional historical exogenous variables. No model training occurs since TabPFN-TS is a zero-shot inference model.

All input normalization and validation is performed upstream by FoundationModel; this method receives canonical dicts only.

Parameters:

Name Type Description Default
context dict pandas Series

Normalized training series, one entry per series.

 required
context_exog dict pandas DataFrame, pandas Series, or None

Per-series historical exogenous variables (past covariates).

 required

Returns:

Name Type Description
self TabPFNAdapter
Source code in skforecast/foundation/_adapters.py 
2077
2078
2079
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
2091
2092
2093
2094
2095
2096
2097
2098
2099
2100
2101
2102
2103
2104
2105
2106
2107
def fit(
    self,
    context: dict[str, pd.Series],
    context_exog: dict[str, pd.DataFrame | pd.Series | None] | None,
) -> TabPFNAdapter:
    """
    Store the training series and optional historical exogenous variables.
    No model training occurs since TabPFN-TS is a zero-shot inference
    model.

    All input normalization and validation is performed upstream by
    `FoundationModel`; this method receives canonical dicts only.

    Parameters
    ----------
    context : dict pandas Series
        Normalized training series, one entry per series.
    context_exog : dict pandas DataFrame, pandas Series, or None
        Per-series historical exogenous variables (past covariates).

    Returns
    -------
    self : TabPFNAdapter

    """

    self.context_      = context
    self.context_exog_ = context_exog
    self.is_fitted     = True

    return self

predict 

predict(steps, context, context_exog, exog, quantiles)

Generate predictions using TabPFN-TS.

All input normalization, validation, and context trimming is performed upstream by FoundationModel; this method receives pre-processed dicts only.

Parameters:

Name Type Description Default
steps int

Number of steps ahead to forecast.

 required
context dict pandas Series

Per-series context windows (already trimmed to context_length).

 required
context_exog dict pandas DataFrame, pandas Series, or None

Per-series past covariates (already trimmed).

 required
exog dict pandas DataFrame, pandas Series, or None

Per-series future covariates for the forecast horizon.

 required
quantiles list of float or None

Quantile levels to return. If None, a point forecast is produced (shape (steps, 1)). Accepts any float in [0, 1].

 required

Returns:

Name Type Description
predictions dict

Keys are series names. Each value is a 2-D numpy ndarray of shape (steps, n_quantiles).
Source code in skforecast/foundation/_adapters.py 
2109
2110
2111
2112
2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
2132
2133
2134
2135
2136
2137
2138
2139
2140
2141
2142
2143
2144
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
2166
2167
2168
2169
2170
2171
2172
2173
2174
2175
2176
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
2189
2190
2191
2192
2193
2194
2195
2196
2197
2198
2199
2200
2201
2202
2203
2204
2205
2206
2207
2208
2209
2210
2211
def predict(
    self,
    steps: int,
    context: dict[str, pd.Series],
    context_exog: dict[str, pd.DataFrame | pd.Series | None] | None,
    exog: dict[str, pd.DataFrame | pd.Series | None] | None,
    quantiles: list[float] | tuple[float] | None,
) -> dict[str, np.ndarray]:
    """
    Generate predictions using TabPFN-TS.

    All input normalization, validation, and context trimming is
    performed upstream by `FoundationModel`; this method receives
    pre-processed dicts only.

    Parameters
    ----------
    steps : int
        Number of steps ahead to forecast.
    context : dict pandas Series
        Per-series context windows (already trimmed to
        `context_length`).
    context_exog : dict pandas DataFrame, pandas Series, or None
        Per-series past covariates (already trimmed).
    exog : dict pandas DataFrame, pandas Series, or None
        Per-series future covariates for the forecast horizon.
    quantiles : list of float or None
        Quantile levels to return. If `None`, a point forecast is
        produced (shape `(steps, 1)`). Accepts any float in `[0, 1]`.

    Returns
    -------
    predictions : dict
        Keys are series names. Each value is a 2-D numpy ndarray of
        shape `(steps, n_quantiles)`.

    """

    self._load_model()

    quantile_list = list(quantiles) if quantiles is not None else None
    tabpfn_quantiles = (
        quantile_list
        if quantile_list is not None
        else [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
    )

    series_names_in = list(context.keys())

    first_series = next(iter(context.values()))
    is_datetime = isinstance(first_series.index, pd.DatetimeIndex)

    if not is_datetime:
        warnings.warn(
            "TabPFNAdapter received series with a non-DatetimeIndex. "
            "TabPFN-TS requires datetime timestamps internally; a "
            "synthetic daily DatetimeIndex (starting 2000-01-01) will be "
            "used. Calendar-based temporal features (CalendarFeature) "
            "will not be meaningful for integer-indexed data. Consider "
            "passing `temporal_features=[]` to disable calendar feature "
            "transforms.",
            # stacklevel=3: TabPFNAdapter.predict → FoundationModel.predict → user
            stacklevel=3,
        )

    context_df = self._build_context_df(
                     series_names = series_names_in,
                     context      = context,
                     context_exog = context_exog,
                     is_datetime  = is_datetime
                 )

    future_df = self._build_future_df(
                    series_names = series_names_in,
                    context      = context,
                    exog         = exog,
                    steps        = steps,
                    is_datetime  = is_datetime
                )

    _stderr_cm = (
        contextlib.redirect_stderr(io.StringIO())
        if not self.show_progress
        else contextlib.nullcontext()
    )
    with _stderr_cm:
        result_df = self._model.predict_df(
                        context_df = context_df,
                        future_df  = future_df,
                        quantiles  = tabpfn_quantiles,
                    )

    # result_df is a DataFrame with MultiIndex (item_id, timestamp).
    # columns: "target" (str) and quantile levels as float column names.
    predictions: dict[str, np.ndarray] = {}
    for name in series_names_in:
        group = result_df.loc[name]  # DataFrame indexed by timestamp
        if quantile_list is None:
            predictions[name] = group["target"].to_numpy().reshape(-1, 1)
        else:
            predictions[name] = group[quantile_list].to_numpy()

    return predictions

_load_model 

_load_model()

Load the TabPFNTSPipeline into self._model if not already set.

Returns:

Type Description
None

Raises:

Type Description
ImportError

If tabpfn-time-series is not installed.
Notes

The pipeline is imported lazily from tabpfn_time_series and instantiated with the current adapter parameters. This method is a no-op when self._model is already populated (either by a prior call or by the model test-injection parameter).

Source code in skforecast/foundation/_adapters.py 
2213
2214
2215
2216
2217
2218
2219
2220
2221
2222
2223
2224
2225
2226
2227
2228
2229
2230
2231
2232
2233
2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
def _load_model(self) -> None:
    """
    Load the `TabPFNTSPipeline` into `self._model` if not already set.

    Returns
    -------
    None

    Raises
    ------
    ImportError
        If `tabpfn-time-series` is not installed.

    Notes
    -----
    The pipeline is imported lazily from `tabpfn_time_series` and
    instantiated with the current adapter parameters. This method is a
    no-op when `self._model` is already populated (either by a prior
    call or by the `model` test-injection parameter).
    """

    if self._model is not None:
        return
    try:
        from tabpfn_time_series import TabPFNMode, TabPFNTSPipeline
    except ImportError as exc:
        raise ImportError(
            "tabpfn-time-series is required for TabPFNAdapter. "
            "Install it with `pip install tabpfn-time-series`."
        ) from exc

    kwargs: dict[str, Any] = {
        "max_context_length": self.context_length,
        "tabpfn_mode": (
            TabPFNMode.LOCAL if self.mode == "local" else TabPFNMode.CLIENT
        ),
        "tabpfn_output_selection": self.point_estimate,
    }
    if self.tabpfn_model_config:
        kwargs["tabpfn_model_config"] = self.tabpfn_model_config
    if self.temporal_features is not None:
        kwargs["temporal_features"] = self.temporal_features

    self._model = TabPFNTSPipeline(**kwargs)

_get_timestamps 

_get_timestamps(series, is_datetime)

Return datetime timestamps for a context series.

For DatetimeIndex series the original index is returned. For RangeIndex series a synthetic daily DatetimeIndex starting at 2000-01-01 is created so that TabPFN-TS's requirement for datetime timestamps is satisfied.

Parameters:

Name Type Description Default
series pandas Series

The context series.

 required
is_datetime bool

Whether the series has a DatetimeIndex.

 required

Returns:

Name Type Description
timestamps pandas DatetimeIndex

Datetime timestamps aligned with the series values.
Source code in skforecast/foundation/_adapters.py 
2258
2259
2260
2261
2262
2263
2264
2265
2266
2267
2268
2269
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
2282
2283
2284
2285
2286
def _get_timestamps(
    self, series: pd.Series, is_datetime: bool
) -> pd.DatetimeIndex:
    """
    Return datetime timestamps for a context series.

    For `DatetimeIndex` series the original index is returned. For
    `RangeIndex` series a synthetic daily `DatetimeIndex` starting at
    2000-01-01 is created so that TabPFN-TS's requirement for datetime
    timestamps is satisfied.

    Parameters
    ----------
    series : pandas Series
        The context series.
    is_datetime : bool
        Whether the series has a `DatetimeIndex`.

    Returns
    -------
    timestamps : pandas DatetimeIndex
        Datetime timestamps aligned with the series values.

    """

    if is_datetime:
        return series.index

    return pd.date_range("2000-01-01", periods=len(series), freq="D")

_get_future_timestamps 

_get_future_timestamps(series, steps, is_datetime)

Return datetime timestamps for the forecast horizon.

For DatetimeIndex series the horizon is appended at the inferred frequency. For RangeIndex series the synthetic daily timeline (2000-01-01 + len(context) days) is extended by steps days.

Parameters:

Name Type Description Default
series pandas Series

The context series (used to determine the end timestamp and frequency).

 required
steps int

Number of steps ahead.

 required
is_datetime bool

Whether the series has a DatetimeIndex.

 required

Returns:

Name Type Description
timestamps pandas DatetimeIndex

Datetime timestamps for the steps forecast steps.
Source code in skforecast/foundation/_adapters.py 
2288
2289
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308
2309
2310
2311
2312
2313
2314
2315
2316
2317
2318
2319
2320
2321
2322
2323
2324
2325
def _get_future_timestamps(
    self, series: pd.Series, steps: int, is_datetime: bool
) -> pd.DatetimeIndex:
    """
    Return datetime timestamps for the forecast horizon.

    For `DatetimeIndex` series the horizon is appended at the inferred
    frequency. For `RangeIndex` series the synthetic daily timeline
    (2000-01-01 + len(context) days) is extended by `steps` days.

    Parameters
    ----------
    series : pandas Series
        The context series (used to determine the end timestamp and
        frequency).
    steps : int
        Number of steps ahead.
    is_datetime : bool
        Whether the series has a `DatetimeIndex`.

    Returns
    -------
    timestamps : pandas DatetimeIndex
        Datetime timestamps for the `steps` forecast steps.

    """

    if is_datetime:
        timestamps = expand_index(series.index, steps=steps)
    else:
        n = len(series)
        timestamps = pd.date_range(
                         start   = pd.Timestamp("2000-01-01") + pd.Timedelta(days=n),
                         periods = steps,
                         freq    = "D",
                     )

    return timestamps

_build_context_df 

_build_context_df(
    series_names, context, context_exog, is_datetime
)

Build a long-format context DataFrame expected by TabPFN-TS.

Each series' observations become rows with item_id, timestamp, target, and optional exogenous covariate columns.

Parameters:

Name Type Description Default
series_names list

Ordered list of series names.

 required
context dict pandas Series

Per-series context windows.

 required
context_exog dict or None

Per-series historical exogenous variables.

 required
is_datetime bool

Whether the series have a DatetimeIndex.

 required

Returns:

Name Type Description
context_df pandas DataFrame

Long-format DataFrame with columns item_id, timestamp, target, and any exogenous columns.
Source code in skforecast/foundation/_adapters.py 
2327
2328
2329
2330
2331
2332
2333
2334
2335
2336
2337
2338
2339
2340
2341
2342
2343
2344
2345
2346
2347
2348
2349
2350
2351
2352
2353
2354
2355
2356
2357
2358
2359
2360
2361
2362
2363
2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
2377
2378
2379
def _build_context_df(
    self,
    series_names: list,
    context: dict[str, pd.Series],
    context_exog: dict[str, pd.DataFrame | None] | None,
    is_datetime: bool,
) -> pd.DataFrame:
    """
    Build a long-format context DataFrame expected by TabPFN-TS.

    Each series' observations become rows with `item_id`, `timestamp`,
    `target`, and optional exogenous covariate columns.

    Parameters
    ----------
    series_names : list
        Ordered list of series names.
    context : dict pandas Series
        Per-series context windows.
    context_exog : dict or None
        Per-series historical exogenous variables.
    is_datetime : bool
        Whether the series have a `DatetimeIndex`.

    Returns
    -------
    context_df : pandas DataFrame
        Long-format DataFrame with columns `item_id`, `timestamp`,
        `target`, and any exogenous columns.

    """

    context_df = []
    for name in series_names:
        series = context[name]
        n = len(series)
        part = pd.DataFrame({
            "item_id":   np.full(n, name),
            "timestamp": np.asarray(self._get_timestamps(series, is_datetime)),
            "target":    series.to_numpy(dtype=float),
        })
        exog_entry = (
            context_exog.get(name) if context_exog is not None else None
        )
        if exog_entry is not None:
            part = pd.concat(
                [part, exog_entry.reset_index(drop=True)], axis=1
            )
        context_df.append(part)

    context_df = pd.concat(context_df, ignore_index=True)

    return context_df

_build_future_df 

_build_future_df(
    series_names, context, exog, steps, is_datetime
)

Build a long-format future DataFrame expected by TabPFN-TS.

Each series' forecast horizon becomes rows with item_id, timestamp, and optional future exogenous covariate columns.

Parameters:

Name Type Description Default
series_names list

Ordered list of series names.

 required
context dict pandas Series

Per-series context windows (used to derive future timestamps).

 required
exog dict or None

Per-series future exogenous variables covering the forecast horizon.

 required
steps int

Number of steps ahead.

 required
is_datetime bool

Whether the series have a DatetimeIndex.

 required

Returns:

Name Type Description
future_df pandas DataFrame

Long-format DataFrame with columns item_id, timestamp, and any future exogenous columns.
Source code in skforecast/foundation/_adapters.py 
2381
2382
2383
2384
2385
2386
2387
2388
2389
2390
2391
2392
2393
2394
2395
2396
2397
2398
2399
2400
2401
2402
2403
2404
2405
2406
2407
2408
2409
2410
2411
2412
2413
2414
2415
2416
2417
2418
2419
2420
2421
2422
2423
2424
2425
2426
2427
2428
2429
2430
2431
2432
2433
2434
2435
def _build_future_df(
    self,
    series_names: list,
    context: dict[str, pd.Series],
    exog: dict[str, pd.DataFrame | None] | None,
    steps: int,
    is_datetime: bool,
) -> pd.DataFrame:
    """
    Build a long-format future DataFrame expected by TabPFN-TS.

    Each series' forecast horizon becomes rows with `item_id`,
    `timestamp`, and optional future exogenous covariate columns.

    Parameters
    ----------
    series_names : list
        Ordered list of series names.
    context : dict pandas Series
        Per-series context windows (used to derive future timestamps).
    exog : dict or None
        Per-series future exogenous variables covering the forecast
        horizon.
    steps : int
        Number of steps ahead.
    is_datetime : bool
        Whether the series have a `DatetimeIndex`.

    Returns
    -------
    future_df : pandas DataFrame
        Long-format DataFrame with columns `item_id`, `timestamp`, and
        any future exogenous columns.

    """

    future_df = []
    for name in series_names:
        series = context[name]
        part = pd.DataFrame({
            "item_id":   np.full(steps, name),
            "timestamp": np.asarray(
                self._get_future_timestamps(series, steps, is_datetime)
            ),
        })
        future_exog = exog.get(name) if exog is not None else None
        if future_exog is not None:
            part = pd.concat(
                [part, future_exog.reset_index(drop=True)], axis=1
            )
        future_df.append(part)

    future_df = pd.concat(future_df, ignore_index=True)

    return future_df

skforecast.foundation._adapters.T0Adapter 

T0Adapter(
    model_id,
    *,
    model=None,
    context_length=8192,
    device_map="auto",
    torch_dtype=None
)

Adapter for The Forecasting Company T0 foundation models.

Parameters:

Name Type Description Default
model_id str

HuggingFace model ID, e.g. "theforecastingcompany/t0-alpha".

 required
model T0Forecaster

Pre-loaded model instance. If None, the model is loaded lazily on the first call to predict.

 None
context_length int

Maximum number of historical observations to use as context. At fit time only the last context_length observations are stored. At predict time, if context is longer than context_length it is trimmed to this length; if it is shorter, all available observations are used as-is. Must be a positive integer.

 8192
device_map str

Device placement for the model. "auto" selects the best available accelerator (CUDA > MPS > CPU). Also accepts explicit values such as "cuda", "mps", or "cpu".

 'auto'
torch_dtype object

Torch dtype the loaded model is cast to (e.g. torch.bfloat16). When None the model keeps its default float32 weights.

 None

Attributes:

Name Type Description
model_id str

HuggingFace model ID.
context_ dict

Stored training series after fitting.
context_exog_ dict

Stored historical exogenous variables after fitting.
context_length int

Maximum number of historical observations used as context.
device_map str

Device map string for model loading.
torch_dtype object

Torch dtype for model loading.
is_fitted bool

Whether the adapter has been fitted.
Notes

T0 conditions on covariates that are known over both the context and the forecast horizon (future-known covariates). skforecast exogenous variables map exactly onto this channel: their historical values (context_exog, aligned to the context) are concatenated with their future values (exog, aligned to the horizon) to form the [context + horizon] covariate stream that T0 expects. Covariates must be numeric; encode categoricals as numbers before passing them. A series with no future exog is forecast without covariates.

T0 checkpoints (e.g. theforecastingcompany/t0-alpha) are gated on the Hugging Face Hub: visit the model page while logged in to accept its license, then authenticate locally (hf auth login or the HF_TOKEN environment variable) before first use.

References

.. [1] https://github.com/theforecastingcompany/tfc-t0

.. [2] https://huggingface.co/theforecastingcompany/t0-alpha

Initialise the adapter.

Parameters:

Name Type Description Default
model_id str

HuggingFace model ID, e.g. "theforecastingcompany/t0-alpha".

 required
model T0Forecaster

Pre-loaded model instance. If None, the model is loaded lazily on the first call to predict.

 None
context_length int

Maximum number of historical observations to retain as context. At fit time only the last context_length observations of series (and exog) are stored. At predict time, if context is longer than context_length it is trimmed to this length before inference; if it is shorter, all available observations are passed as-is. Must be a positive integer.

 8192
device_map str

Device placement for the model. "auto" selects the best available accelerator (CUDA > MPS > CPU). Also accepts explicit values such as "cuda", "mps", or "cpu".

 'auto'
torch_dtype object

Torch dtype the loaded model is cast to (e.g. torch.bfloat16). When None the model keeps its default float32 weights.

 None

Methods:

Name Description
get_params

Return the adapter's constructor parameters.
set_params

Set adapter parameters. Resets the model when a device, dtype, or
fit

Store the training series and optional historical exogenous variables.
predict

Generate predictions using the T0 model.
Source code in skforecast/foundation/_adapters.py 
2506
2507
2508
2509
2510
2511
2512
2513
2514
2515
2516
2517
2518
2519
2520
2521
2522
2523
2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
2535
2536
2537
2538
2539
2540
2541
2542
2543
2544
2545
2546
2547
2548
2549
2550
2551
def __init__(
    self,
    model_id: str,
    *,
    model: Any | None = None,
    context_length: int = 8192,
    device_map: str = "auto",
    torch_dtype: Any | None = None,
) -> None:
    """
    Initialise the adapter.

    Parameters
    ----------
    model_id : str
        HuggingFace model ID, e.g. "theforecastingcompany/t0-alpha".
    model : T0Forecaster, default None
        Pre-loaded model instance. If `None`, the model is loaded
        lazily on the first call to `predict`.
    context_length : int, default 8192
        Maximum number of historical observations to retain as context.
        At `fit` time only the last `context_length` observations of
        `series` (and `exog`) are stored. At `predict` time, if `context`
        is longer than `context_length` it is trimmed to this length
        before inference; if it is shorter, all available observations
        are passed as-is. Must be a positive integer.
    device_map : str, default 'auto'
        Device placement for the model. `"auto"` selects the best
        available accelerator (CUDA > MPS > CPU). Also accepts explicit
        values such as `"cuda"`, `"mps"`, or `"cpu"`.
    torch_dtype : object, default None
        Torch dtype the loaded model is cast to (e.g. `torch.bfloat16`).
        When `None` the model keeps its default `float32` weights.

    """

    _validate_positive_int("context_length", context_length)

    self.model_id       = model_id
    self._model         = model
    self.context_       = None
    self.context_exog_  = None
    self.context_length = context_length
    self.device_map     = device_map
    self.torch_dtype    = torch_dtype
    self.is_fitted      = False

Attributes

allow_exog class-attribute instance-attribute 

allow_exog = True

model_id instance-attribute 

model_id = model_id

_model instance-attribute 

_model = model

context_ instance-attribute 

context_ = None

context_exog_ instance-attribute 

context_exog_ = None

context_length instance-attribute 

context_length = context_length

device_map instance-attribute 

device_map = device_map

torch_dtype instance-attribute 

torch_dtype = torch_dtype

is_fitted instance-attribute 

is_fitted = False

Methods:

get_params 

get_params()

Return the adapter's constructor parameters.

Returns:

Name Type Description
params dict

Keys: model_id, context_length, device_map, torch_dtype.
Source code in skforecast/foundation/_adapters.py 
2553
2554
2555
2556
2557
2558
2559
2560
2561
2562
2563
2564
2565
2566
2567
2568
def get_params(self) -> dict:
    """
    Return the adapter's constructor parameters.

    Returns
    -------
    params : dict
        Keys: `model_id`, `context_length`, `device_map`, `torch_dtype`.

    """
    return {
        'model_id':       self.model_id,
        'context_length': self.context_length,
        'device_map':     self.device_map,
        'torch_dtype':    self.torch_dtype,
    }

set_params 

set_params(**params)

Set adapter parameters. Resets the model when a device, dtype, or model_id param changes, since those are baked into the loaded model.

Parameters:

Name Type Description Default
**params

Valid keys: model_id, context_length, device_map, torch_dtype.

 {}

Returns:

Name Type Description
self T0Adapter
Source code in skforecast/foundation/_adapters.py 
2570
2571
2572
2573
2574
2575
2576
2577
2578
2579
2580
2581
2582
2583
2584
2585
2586
2587
2588
2589
2590
2591
2592
2593
2594
2595
2596
2597
2598
2599
2600
2601
def set_params(self, **params) -> T0Adapter:
    """
    Set adapter parameters. Resets the model when a device, dtype, or
    model_id param changes, since those are baked into the loaded model.

    Parameters
    ----------
    **params :
        Valid keys: `model_id`, `context_length`, `device_map`,
        `torch_dtype`.

    Returns
    -------
    self : T0Adapter

    """

    def validate(p: dict) -> dict:
        if "context_length" in p:
            _validate_positive_int("context_length", p["context_length"])
        return p

    return _apply_set_params(
        self, params,
        validate=validate,
        resets=(
            (
                {"model_id", "device_map", "torch_dtype"},
                lambda: setattr(self, "_model", None),
            ),
        ),
    )

fit 

fit(context, context_exog)

Store the training series and optional historical exogenous variables. No model training occurs since T0 is a zero-shot inference model.

All input normalization and validation is performed upstream by FoundationModel; this method receives canonical dicts only.

Parameters:

Name Type Description Default
context dict pandas Series

Normalized training series, one entry per series.

 required
context_exog dict pandas DataFrame, pandas Series, or None

Per-series historical exogenous variables (past covariates).

 required

Returns:

Name Type Description
self T0Adapter
Source code in skforecast/foundation/_adapters.py 
2603
2604
2605
2606
2607
2608
2609
2610
2611
2612
2613
2614
2615
2616
2617
2618
2619
2620
2621
2622
2623
2624
2625
2626
2627
2628
2629
2630
2631
2632
def fit(
    self,
    context: dict[str, pd.Series],
    context_exog: dict[str, pd.DataFrame | pd.Series | None],
) -> T0Adapter:
    """
    Store the training series and optional historical exogenous variables.
    No model training occurs since T0 is a zero-shot inference model.

    All input normalization and validation is performed upstream by
    `FoundationModel`; this method receives canonical dicts only.

    Parameters
    ----------
    context : dict pandas Series
        Normalized training series, one entry per series.
    context_exog : dict pandas DataFrame, pandas Series, or None
        Per-series historical exogenous variables (past covariates).

    Returns
    -------
    self : T0Adapter

    """

    self.context_ = context
    self.context_exog_ = context_exog
    self.is_fitted = True

    return self

predict 

predict(steps, context, context_exog, exog, quantiles)

Generate predictions using the T0 model.

All input normalization, validation, and context trimming is performed upstream by FoundationModel; this method receives pre-processed dicts only.

Parameters:

Name Type Description Default
steps int

Number of steps ahead to forecast.

 required
context dict

Per-series context windows (already trimmed to context_length).

 required
context_exog dict

Per-series past covariates (already trimmed).

 required
exog dict

Per-series future covariates for the forecast horizon.

 required
quantiles list of float or None

Quantile levels to return, in the requested order. If None, a point forecast (median, quantile 0.5) is produced.

 required

Returns:

Name Type Description
predictions dict

Keys are series names. Each value is a 2-D array of shape (steps, n_quantiles) with columns ordered to match quantiles.
Source code in skforecast/foundation/_adapters.py 
2634
2635
2636
2637
2638
2639
2640
2641
2642
2643
2644
2645
2646
2647
2648
2649
2650
2651
2652
2653
2654
2655
2656
2657
2658
2659
2660
2661
2662
2663
2664
2665
2666
2667
2668
2669
2670
2671
2672
2673
2674
2675
2676
2677
2678
2679
2680
2681
2682
2683
2684
2685
2686
2687
2688
2689
2690
2691
2692
2693
2694
2695
2696
2697
2698
2699
2700
2701
2702
2703
2704
2705
2706
2707
2708
2709
2710
2711
def predict(
    self,
    steps: int,
    context: dict[str, pd.Series],
    context_exog: dict[str, pd.DataFrame | pd.Series | None],
    exog: dict[str, pd.DataFrame | pd.Series | None],
    quantiles: list[float] | tuple[float] | None
) -> dict[str, np.ndarray]:
    """
    Generate predictions using the T0 model.

    All input normalization, validation, and context trimming is
    performed upstream by `FoundationModel`; this method receives
    pre-processed dicts only.

    Parameters
    ----------
    steps : int
        Number of steps ahead to forecast.
    context : dict
        Per-series context windows (already trimmed to `context_length`).
    context_exog : dict
        Per-series past covariates (already trimmed).
    exog : dict
        Per-series future covariates for the forecast horizon.
    quantiles : list of float or None
        Quantile levels to return, in the requested order. If `None`, a
        point forecast (median, quantile 0.5) is produced.

    Returns
    -------
    predictions : dict
        Keys are series names. Each value is a 2-D array of shape
        `(steps, n_quantiles)` with columns ordered to match `quantiles`.

    """

    # NOTE: the model is loaded lazily here so that the adapter can be
    # instantiated and fitted without requiring tfc-t0 to be installed.
    self._load_model()

    requested = list(quantiles) if quantiles is not None else [0.5]
    # T0 requires sorted, unique levels in (0, 1); query those, then
    # reindex the columns back to the order the caller asked for.
    query_levels = sorted(set(requested))

    series_names = list(context.keys())
    arrays = [np.asarray(context[name].to_numpy(), dtype=np.float32) for name in series_names]
    lengths = [a.shape[0] for a in arrays]
    context_length = max(lengths)

    # All series are forecast in a single batched call. Series shorter than
    # the longest are left-padded with NaN, which T0 treats as MISSING; the
    # forecast origin therefore aligns at the end of the window for every
    # series.
    context_batch = np.full((len(series_names), context_length), np.nan, dtype=np.float32)
    for row, array in zip(context_batch, arrays):
        row[context_length - array.shape[0]:] = array

    future_covariates = self._build_future_covariates(
        series_names   = series_names,
        context_exog   = context_exog,
        exog           = exog,
        context_length = context_length,
        steps          = steps,
    )

    forecast = self._model.predict(
        context           = context_batch,
        horizon           = steps,
        quantiles         = query_levels,
        future_covariates = future_covariates,
    )

    q_arr = _tensor_to_numpy(forecast.quantiles)

    column_for = [query_levels.index(q) for q in requested]
    return {name: q_arr[i][:, column_for] for i, name in enumerate(series_names)}

_load_model 

_load_model()

Load the T0 model into self._model if not already set.

Returns:

Type Description
None

Raises:

Type Description
ImportError

If tfc-t0 is not installed.
OSError

If T0Forecaster.from_pretrained fails to build the model, most commonly because the repository is gated on the Hugging Face Hub and the active credentials have not accepted its license.
Notes

The model is imported lazily from t0 and loaded via T0Forecaster.from_pretrained, then moved to the resolved device and switched to eval mode. This method is a no-op when self._model is already populated.

T0 checkpoints are gated on the Hugging Face Hub. When the repository's config.json cannot be downloaded (e.g. the license has not been accepted, or no valid token is available), huggingface_hub's from_pretrained silently swallows the download failure and falls back to instantiating the model with no constructor arguments, raising a confusing TypeError about missing hyperparameters. That TypeError is caught here and re-raised as a clearer OSError pointing to the likely cause.

Source code in skforecast/foundation/_adapters.py 
2713
2714
2715
2716
2717
2718
2719
2720
2721
2722
2723
2724
2725
2726
2727
2728
2729
2730
2731
2732
2733
2734
2735
2736
2737
2738
2739
2740
2741
2742
2743
2744
2745
2746
2747
2748
2749
2750
2751
2752
2753
2754
2755
2756
2757
2758
2759
2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
2771
2772
2773
2774
def _load_model(self) -> None:
    """
    Load the T0 model into `self._model` if not already set.

    Returns
    -------
    None

    Raises
    ------
    ImportError
        If `tfc-t0` is not installed.
    OSError
        If `T0Forecaster.from_pretrained` fails to build the model, most
        commonly because the repository is gated on the Hugging Face Hub
        and the active credentials have not accepted its license.

    Notes
    -----
    The model is imported lazily from `t0` and loaded via
    `T0Forecaster.from_pretrained`, then moved to the resolved device and
    switched to eval mode. This method is a no-op when `self._model` is
    already populated.

    T0 checkpoints are gated on the Hugging Face Hub. When the
    repository's `config.json` cannot be downloaded (e.g. the license
    has not been accepted, or no valid token is available),
    `huggingface_hub`'s `from_pretrained` silently swallows the download
    failure and falls back to instantiating the model with no
    constructor arguments, raising a confusing `TypeError` about missing
    hyperparameters. That `TypeError` is caught here and re-raised as a
    clearer `OSError` pointing to the likely cause.

    """

    if self._model is not None:
        return
    try:
        from t0 import T0Forecaster
    except ImportError as exc:
        raise ImportError(
            "tfc-t0 is required for T0Adapter. "
            "Install it with `pip install tfc-t0`."
        ) from exc

    try:
        model = T0Forecaster.from_pretrained(self.model_id)
    except TypeError as exc:
        raise OSError(
            f"Could not load model '{self.model_id}' from the Hugging "
            f"Face Hub. This is often caused by a gated repository "
            f"whose license has not been accepted: visit "
            f"https://huggingface.co/{self.model_id} while logged in "
            f"to accept it, then authenticate locally (`hf auth login` "
            f"or the `HF_TOKEN` environment variable) before retrying."
        ) from exc

    device = _resolve_torch_device(self.device_map)
    model = model.to(device)
    if self.torch_dtype is not None:
        model = model.to(self.torch_dtype)
    self._model = model.eval()

_build_future_covariates 

_build_future_covariates(
    series_names, context_exog, exog, context_length, steps
)

Assemble T0's batched [n_series, n_covariates, context_length + steps] covariate array from per-series past and future exogenous values.

Covariate columns are pooled across all series (first-seen order). For each series and column the historical values (from context_exog) are placed flush against the forecast origin and the future values (from exog) cover the horizon. Every unfilled cell — a padded timestep, or a column/series that lacks that covariate — stays NaN, which T0 treats as missing.

Parameters:

Name Type Description Default
series_names list of str

Series order defining the batch rows.

 required
context_exog dict or None

Per-series historical exogenous values aligned to each context.

 required
exog dict or None

Per-series future-known exogenous values covering the horizon.

 required
context_length int

Width of the (left-padded) context window.

 required
steps int

Number of forecast steps.

 required

Returns:

Name Type Description
future_covariates numpy ndarray or None

Array of shape (n_series, n_covariates, context_length + steps), or None when no series has future exog.
Source code in skforecast/foundation/_adapters.py 
2776
2777
2778
2779
2780
2781
2782
2783
2784
2785
2786
2787
2788
2789
2790
2791
2792
2793
2794
2795
2796
2797
2798
2799
2800
2801
2802
2803
2804
2805
2806
2807
2808
2809
2810
2811
2812
2813
2814
2815
2816
2817
2818
2819
2820
2821
2822
2823
2824
2825
2826
2827
2828
2829
2830
2831
2832
2833
2834
2835
2836
2837
2838
2839
2840
2841
2842
2843
2844
2845
2846
2847
2848
2849
2850
2851
2852
2853
2854
def _build_future_covariates(
    self,
    series_names: list[str],
    context_exog: dict[str, pd.DataFrame | pd.Series | None] | None,
    exog: dict[str, pd.DataFrame | pd.Series | None] | None,
    context_length: int,
    steps: int,
) -> np.ndarray | None:
    """
    Assemble T0's batched `[n_series, n_covariates, context_length + steps]`
    covariate array from per-series past and future exogenous values.

    Covariate columns are pooled across all series (first-seen order). For
    each series and column the historical values (from `context_exog`) are
    placed flush against the forecast origin and the future values (from
    `exog`) cover the horizon. Every unfilled cell — a padded timestep, or a
    column/series that lacks that covariate — stays NaN, which T0 treats as
    missing.

    Parameters
    ----------
    series_names : list of str
        Series order defining the batch rows.
    context_exog : dict or None
        Per-series historical exogenous values aligned to each context.
    exog : dict or None
        Per-series future-known exogenous values covering the horizon.
    context_length : int
        Width of the (left-padded) context window.
    steps : int
        Number of forecast steps.

    Returns
    -------
    future_covariates : numpy ndarray or None
        Array of shape `(n_series, n_covariates, context_length + steps)`,
        or `None` when no series has future exog.

    """

    if exog is None:
        return None

    future_frames = {
        name: (e if isinstance(e, pd.DataFrame) else e.to_frame())
        for name, e in exog.items()
        if e is not None
    }
    if not future_frames:
        return None

    columns: list[str] = list(
        dict.fromkeys(
            col for frame in future_frames.values() for col in frame.columns
        )
    )

    total_length = context_length + steps
    covariates = np.full(
        (len(series_names), len(columns), total_length), np.nan, dtype=np.float32
    )
    column_index = {col: j for j, col in enumerate(columns)}
    for row, name in enumerate(series_names):
        future_df = future_frames.get(name)
        if future_df is None:
            continue
        past_df = None
        if context_exog is not None and context_exog.get(name) is not None:
            ctx = context_exog[name]
            past_df = ctx if isinstance(ctx, pd.DataFrame) else ctx.to_frame()
        for col in future_df.columns:
            j = column_index[col]
            future_values = self._to_float_array(future_df[col])
            covariates[row, j, context_length:context_length + future_values.shape[0]] = future_values
            if past_df is not None and col in past_df.columns:
                past_values = self._to_float_array(past_df[col])
                covariates[row, j, context_length - past_values.shape[0]:context_length] = past_values

    return covariates

_to_float_array staticmethod 

_to_float_array(col_data)

Convert a numeric or boolean covariate column to a float32 array.

Parameters:

Name Type Description Default
col_data pandas Series

A single covariate column.

 required

Returns:

Name Type Description
col_array numpy ndarray

1-D float32 array.

Raises:

Type Description
ValueError

If the column is neither numeric nor boolean. T0 only conditions on numeric covariates; categoricals must be encoded as numbers.
Source code in skforecast/foundation/_adapters.py 
2856
2857
2858
2859
2860
2861
2862
2863
2864
2865
2866
2867
2868
2869
2870
2871
2872
2873
2874
2875
2876
2877
2878
2879
2880
2881
2882
2883
2884
2885
2886
@staticmethod
def _to_float_array(col_data: pd.Series) -> np.ndarray:
    """
    Convert a numeric or boolean covariate column to a `float32` array.

    Parameters
    ----------
    col_data : pandas Series
        A single covariate column.

    Returns
    -------
    col_array : numpy ndarray
        1-D `float32` array.

    Raises
    ------
    ValueError
        If the column is neither numeric nor boolean. T0 only conditions
        on numeric covariates; categoricals must be encoded as numbers.

    """

    if pd.api.types.is_numeric_dtype(col_data) or pd.api.types.is_bool_dtype(col_data):
        return col_data.astype(np.float32).to_numpy()

    raise ValueError(
        f"T0Adapter supports only numeric covariates. Column "
        f"{col_data.name!r} has dtype {col_data.dtype}. Encode categorical "
        f"covariates as numeric values before passing them."
    )

skforecast.foundation._adapters.NoriAdapter 

NoriAdapter(
    model_id,
    *,
    model=None,
    context_length=4096,
    point_estimate="mean",
    add_calendar_features=True,
    n_fourier_terms=2,
    nori_config=None
)

Adapter for Synthefy Nori zero-shot tabular foundation models.

Nori is a tabular regression foundation model that predicts via in-context learning: given labeled context rows it predicts query rows in a single forward pass, with no task-specific training or fine-tuning. This adapter frames forecasting as tabular regression: each series is featurized (running index, calendar features, Fourier seasonal terms, and optional known-future covariates) and a NoriRegressor predicts the forecast horizon zero-shot.

Parameters:

Name Type Description Default
model_id str

Model ID, e.g. "Synthefy/Nori". Used to resolve this adapter; the underlying checkpoint is controlled by nori_config (key model_path) and defaults to the public HuggingFace checkpoint.

 required
model object

Pre-instantiated NoriRegressor instance. If None, a new instance is created lazily on the first call to predict. Intended for testing only.

 None
context_length int

Maximum number of historical observations to use as context rows. At fit time only the last context_length observations are stored. At predict time, if context is longer than context_length it is trimmed to this length; if it is shorter, all available observations are used as-is. Must be a positive integer.

 4096
point_estimate str

Method used to derive the point forecast from Nori's predictive distribution. Accepted values: 'mean', 'median', 'mode'.

 'mean'
add_calendar_features bool

If True, add calendar features (month, day, day-of-week, day-of-year, quarter, hour) when the series has a DatetimeIndex. Ignored for RangeIndex series.

 True
n_fourier_terms int

Number of Fourier (sin/cos) seasonal harmonics added on the yearly and weekly cycles for datetime series (or on the running index for RangeIndex series). Set 0 to disable. Must be a non-negative integer.

 2
nori_config dict

Additional keyword arguments forwarded verbatim to NoriRegressor at instantiation (e.g. model_path, device, token, augmentations). If None, the library defaults are used.

 None

Attributes:

Name Type Description
model_id str

Model ID.
context_ dict

Stored training series after fitting.
context_exog_ dict

Stored historical exogenous variables after fitting.
context_length int

Maximum number of historical observations used as context.
point_estimate str

Point forecast method.
add_calendar_features bool

Whether calendar features are added for datetime series.
n_fourier_terms int

Number of Fourier seasonal harmonics added.
nori_config dict

Additional configuration forwarded to NoriRegressor.
is_fitted bool

Whether the adapter has been fitted.
_model object

Internal NoriRegressor instance. None until the first call to predict, after which it is cached for reuse.
Notes

Nori supports arbitrary quantile levels (any float strictly in (0, 1)), unlike models with fixed quantile sets such as TimesFM or Moirai. A bar_distribution checkpoint does not support quantiles.

Covariate support is available for known-future covariates: columns present in both the historical context and the forecast horizon are used as features. Covariates without future values are ignored. Covariates must be numeric; encode categoricals as numbers before passing them.

Series with a RangeIndex are accepted; only running-index and Fourier(index) features are meaningful there (calendar features are skipped).

References

.. [1] https://github.com/Synthefy/synthefy-nori

.. [2] https://huggingface.co/Synthefy/Nori

.. [3] https://docs.synthefy.com/nori/

Initialise the adapter.

Parameters:

Name Type Description Default
model_id str

Model ID, e.g. "Synthefy/Nori".

 required
model object

Pre-instantiated NoriRegressor instance. If None, a new instance is created lazily on the first call to predict. Intended for testing only.

 None
context_length int

Maximum number of historical observations to retain as context. At fit time only the last context_length observations of series (and exog) are stored. At predict time, if context is longer than context_length it is trimmed to this length before inference; if it is shorter, all available observations are passed as-is. Must be a positive integer.

 4096
point_estimate str

Method used to derive the point forecast. Accepted values: 'mean', 'median', 'mode'.

 'mean'
add_calendar_features bool

If True, add calendar features when the series has a DatetimeIndex. Ignored for RangeIndex series.

 True
n_fourier_terms int

Number of Fourier seasonal harmonics added. Set 0 to disable. Must be a non-negative integer.

 2
nori_config dict

Additional keyword arguments forwarded verbatim to NoriRegressor at instantiation.

 None

Methods:

Name Description
get_params

Return the adapter's constructor parameters.
set_params

Set adapter parameters. Resets the loaded model when a parameter baked
fit

Store the training series and optional historical exogenous variables.
predict

Generate predictions using Nori.
Source code in skforecast/foundation/_adapters.py 
3413
3414
3415
3416
3417
3418
3419
3420
3421
3422
3423
3424
3425
3426
3427
3428
3429
3430
3431
3432
3433
3434
3435
3436
3437
3438
3439
3440
3441
3442
3443
3444
3445
3446
3447
3448
3449
3450
3451
3452
3453
3454
3455
3456
3457
3458
3459
3460
3461
3462
3463
3464
3465
3466
3467
3468
3469
3470
3471
3472
3473
3474
3475
3476
3477
3478
3479
3480
3481
3482
3483
def __init__(
    self,
    model_id: str,
    *,
    model: Any | None = None,
    context_length: int = 4096,
    point_estimate: str = "mean",
    add_calendar_features: bool = True,
    n_fourier_terms: int = 2,
    nori_config: dict[str, Any] | None = None,
) -> None:
    """
    Initialise the adapter.

    Parameters
    ----------
    model_id : str
        Model ID, e.g. `"Synthefy/Nori"`.
    model : object, default None
        Pre-instantiated `NoriRegressor` instance. If `None`, a new
        instance is created lazily on the first call to `predict`.
        Intended for testing only.
    context_length : int, default 4096
        Maximum number of historical observations to retain as context.
        At `fit` time only the last `context_length` observations of
        `series` (and `exog`) are stored. At `predict` time, if `context`
        is longer than `context_length` it is trimmed to this length
        before inference; if it is shorter, all available observations are
        passed as-is. Must be a positive integer.
    point_estimate : str, default 'mean'
        Method used to derive the point forecast. Accepted values:
        `'mean'`, `'median'`, `'mode'`.
    add_calendar_features : bool, default True
        If `True`, add calendar features when the series has a
        `DatetimeIndex`. Ignored for `RangeIndex` series.
    n_fourier_terms : int, default 2
        Number of Fourier seasonal harmonics added. Set `0` to disable.
        Must be a non-negative integer.
    nori_config : dict, default None
        Additional keyword arguments forwarded verbatim to `NoriRegressor`
        at instantiation.

    """

    _validate_positive_int("context_length", context_length)
    if point_estimate not in ("mean", "median", "mode"):
        raise ValueError(
            f"`point_estimate` must be 'mean', 'median' or 'mode'. "
            f"Got {point_estimate!r}."
        )
    if not isinstance(add_calendar_features, bool):
        raise ValueError(
            f"`add_calendar_features` must be a bool. "
            f"Got {add_calendar_features!r}."
        )
    if not isinstance(n_fourier_terms, int) or n_fourier_terms < 0:
        raise ValueError(
            f"`n_fourier_terms` must be a non-negative integer. "
            f"Got {n_fourier_terms!r}."
        )

    self.model_id              = model_id
    self._model                = model
    self.context_              = None
    self.context_exog_         = None
    self.context_length        = context_length
    self.point_estimate        = point_estimate
    self.add_calendar_features = add_calendar_features
    self.n_fourier_terms       = n_fourier_terms
    self.nori_config           = nori_config or {}
    self.is_fitted             = False

Attributes

allow_exog class-attribute instance-attribute 

allow_exog = True

model_id instance-attribute 

model_id = model_id

_model instance-attribute 

_model = model

context_ instance-attribute 

context_ = None

context_exog_ instance-attribute 

context_exog_ = None

context_length instance-attribute 

context_length = context_length

point_estimate instance-attribute 

point_estimate = point_estimate

add_calendar_features instance-attribute 

add_calendar_features = add_calendar_features

n_fourier_terms instance-attribute 

n_fourier_terms = n_fourier_terms

nori_config instance-attribute 

nori_config = nori_config or {}

is_fitted instance-attribute 

is_fitted = False

Methods:

get_params 

get_params()

Return the adapter's constructor parameters.

Returns:

Name Type Description
params dict

Keys: model_id, context_length, point_estimate, add_calendar_features, n_fourier_terms, nori_config. nori_config is returned as None when no additional config was set (i.e. when the internal dict is empty).
Source code in skforecast/foundation/_adapters.py 
3485
3486
3487
3488
3489
3490
3491
3492
3493
3494
3495
3496
3497
3498
3499
3500
3501
3502
3503
3504
3505
def get_params(self) -> dict:
    """
    Return the adapter's constructor parameters.

    Returns
    -------
    params : dict
        Keys: `model_id`, `context_length`, `point_estimate`,
        `add_calendar_features`, `n_fourier_terms`, `nori_config`.
        `nori_config` is returned as `None` when no additional config was
        set (i.e. when the internal dict is empty).

    """
    return {
        "model_id":              self.model_id,
        "context_length":        self.context_length,
        "point_estimate":        self.point_estimate,
        "add_calendar_features": self.add_calendar_features,
        "n_fourier_terms":       self.n_fourier_terms,
        "nori_config":           self.nori_config or None,
    }

set_params 

set_params(**params)

Set adapter parameters. Resets the loaded model when a parameter baked into the NoriRegressor instance changes (model_id, nori_config); featurization/inference-time parameters (context_length, point_estimate, add_calendar_features, n_fourier_terms) do not reset the model.

Parameters:

Name Type Description Default
**params

Valid keys: model_id, context_length, point_estimate, add_calendar_features, n_fourier_terms, nori_config.

 {}

Returns:

Name Type Description
self NoriAdapter
Source code in skforecast/foundation/_adapters.py 
3507
3508
3509
3510
3511
3512
3513
3514
3515
3516
3517
3518
3519
3520
3521
3522
3523
3524
3525
3526
3527
3528
3529
3530
3531
3532
3533
3534
3535
3536
3537
3538
3539
3540
3541
3542
3543
3544
3545
3546
3547
3548
3549
3550
3551
3552
3553
3554
3555
3556
3557
def set_params(self, **params) -> NoriAdapter:
    """
    Set adapter parameters. Resets the loaded model when a parameter baked
    into the `NoriRegressor` instance changes (`model_id`, `nori_config`);
    featurization/inference-time parameters (`context_length`,
    `point_estimate`, `add_calendar_features`, `n_fourier_terms`) do not
    reset the model.

    Parameters
    ----------
    **params :
        Valid keys: `model_id`, `context_length`, `point_estimate`,
        `add_calendar_features`, `n_fourier_terms`, `nori_config`.

    Returns
    -------
    self : NoriAdapter

    """

    def validate(p: dict) -> dict:
        if "context_length" in p:
            _validate_positive_int("context_length", p["context_length"])
        if "point_estimate" in p and p["point_estimate"] not in ("mean", "median", "mode"):
            raise ValueError(
                f"`point_estimate` must be 'mean', 'median' or 'mode'. "
                f"Got {p['point_estimate']!r}."
            )
        if "add_calendar_features" in p and not isinstance(p["add_calendar_features"], bool):
            raise ValueError(
                f"`add_calendar_features` must be a bool. "
                f"Got {p['add_calendar_features']!r}."
            )
        if "n_fourier_terms" in p and (
            not isinstance(p["n_fourier_terms"], int) or p["n_fourier_terms"] < 0
        ):
            raise ValueError(
                f"`n_fourier_terms` must be a non-negative integer. "
                f"Got {p['n_fourier_terms']!r}."
            )
        if "nori_config" in p:
            p["nori_config"] = p["nori_config"] or {}
        return p

    return _apply_set_params(
        self, params,
        validate=validate,
        resets=(
            ({"model_id", "nori_config"}, lambda: setattr(self, "_model", None)),
        ),
    )

fit 

fit(context, context_exog)

Store the training series and optional historical exogenous variables. No model training occurs since Nori is a zero-shot inference model.

All input normalization and validation is performed upstream by FoundationModel; this method receives canonical dicts only.

Parameters:

Name Type Description Default
context dict pandas Series

Normalized training series, one entry per series.

 required
context_exog dict pandas DataFrame, pandas Series, or None

Per-series historical exogenous variables (past covariates).

 required

Returns:

Name Type Description
self NoriAdapter
Source code in skforecast/foundation/_adapters.py 
3559
3560
3561
3562
3563
3564
3565
3566
3567
3568
3569
3570
3571
3572
3573
3574
3575
3576
3577
3578
3579
3580
3581
3582
3583
3584
3585
3586
3587
3588
def fit(
    self,
    context: dict[str, pd.Series],
    context_exog: dict[str, pd.DataFrame | pd.Series | None] | None,
) -> NoriAdapter:
    """
    Store the training series and optional historical exogenous variables.
    No model training occurs since Nori is a zero-shot inference model.

    All input normalization and validation is performed upstream by
    `FoundationModel`; this method receives canonical dicts only.

    Parameters
    ----------
    context : dict pandas Series
        Normalized training series, one entry per series.
    context_exog : dict pandas DataFrame, pandas Series, or None
        Per-series historical exogenous variables (past covariates).

    Returns
    -------
    self : NoriAdapter

    """

    self.context_      = context
    self.context_exog_ = context_exog
    self.is_fitted     = True

    return self

predict 

predict(steps, context, context_exog, exog, quantiles)

Generate predictions using Nori.

All input normalization, validation, and context trimming is performed upstream by FoundationModel; this method receives pre-processed dicts only.

Parameters:

Name Type Description Default
steps int

Number of steps ahead to forecast.

 required
context dict pandas Series

Per-series context windows (already trimmed to context_length).

 required
context_exog dict pandas DataFrame, pandas Series, or None

Per-series past covariates (already trimmed).

 required
exog dict pandas DataFrame, pandas Series, or None

Per-series future covariates for the forecast horizon.

 required
quantiles list of float or None

Quantile levels to return, in the requested order. Must lie strictly in (0, 1). If None, a point forecast is produced (shape (steps, 1)).

 required

Returns:

Name Type Description
predictions dict

Keys are series names. Each value is a 2-D numpy ndarray of shape (steps, n_quantiles) with columns ordered to match quantiles.

Raises:

Type Description
ValueError

If a requested quantile level is not strictly in (0, 1).
Source code in skforecast/foundation/_adapters.py 
3590
3591
3592
3593
3594
3595
3596
3597
3598
3599
3600
3601
3602
3603
3604
3605
3606
3607
3608
3609
3610
3611
3612
3613
3614
3615
3616
3617
3618
3619
3620
3621
3622
3623
3624
3625
3626
3627
3628
3629
3630
3631
3632
3633
3634
3635
3636
3637
3638
3639
3640
3641
3642
3643
3644
3645
3646
3647
3648
3649
3650
3651
3652
3653
3654
3655
3656
3657
3658
3659
3660
3661
3662
3663
3664
3665
3666
3667
3668
3669
3670
3671
3672
3673
3674
3675
3676
3677
3678
3679
3680
3681
3682
3683
3684
3685
3686
3687
3688
3689
3690
3691
3692
3693
3694
def predict(
    self,
    steps: int,
    context: dict[str, pd.Series],
    context_exog: dict[str, pd.DataFrame | pd.Series | None] | None,
    exog: dict[str, pd.DataFrame | pd.Series | None] | None,
    quantiles: list[float] | tuple[float] | None,
) -> dict[str, np.ndarray]:
    """
    Generate predictions using Nori.

    All input normalization, validation, and context trimming is
    performed upstream by `FoundationModel`; this method receives
    pre-processed dicts only.

    Parameters
    ----------
    steps : int
        Number of steps ahead to forecast.
    context : dict pandas Series
        Per-series context windows (already trimmed to `context_length`).
    context_exog : dict pandas DataFrame, pandas Series, or None
        Per-series past covariates (already trimmed).
    exog : dict pandas DataFrame, pandas Series, or None
        Per-series future covariates for the forecast horizon.
    quantiles : list of float or None
        Quantile levels to return, in the requested order. Must lie
        strictly in `(0, 1)`. If `None`, a point forecast is produced
        (shape `(steps, 1)`).

    Returns
    -------
    predictions : dict
        Keys are series names. Each value is a 2-D numpy ndarray of shape
        `(steps, n_quantiles)` with columns ordered to match `quantiles`.

    Raises
    ------
    ValueError
        If a requested quantile level is not strictly in `(0, 1)`.

    """

    quantile_list = list(quantiles) if quantiles is not None else None
    if quantile_list is not None and any(
        (q <= 0.0) or (q >= 1.0) for q in quantile_list
    ):
        raise ValueError(
            "NoriAdapter quantiles must lie strictly in (0, 1). "
            f"Got {quantile_list!r}."
        )

    # Nori does not guarantee that the output column order matches the
    # requested quantiles, so query sorted, unique levels and reindex the
    # columns back to the caller's order afterwards.
    if quantile_list is not None:
        query_levels = sorted(set(quantile_list))
        column_for = [query_levels.index(q) for q in quantile_list]

    self._load_model()

    first_series = next(iter(context.values()))
    is_datetime = isinstance(first_series.index, pd.DatetimeIndex)
    if not is_datetime and self.add_calendar_features:
        warnings.warn(
            "NoriAdapter received series with a non-DatetimeIndex; "
            "calendar features are skipped. Only running-index and "
            "Fourier(index) features are used.",
            # stacklevel=3: NoriAdapter.predict → FoundationModel.predict → user
            stacklevel=3,
        )

    predictions: dict[str, np.ndarray] = {}
    for name, series in context.items():
        ctx_exog = context_exog.get(name) if context_exog is not None else None
        fut_exog = exog.get(name) if exog is not None else None
        exog_cols = self._known_future_columns(ctx_exog, fut_exog)

        X_ctx = self._featurize(
            series, is_datetime, ctx_exog, exog_cols, offset=0, n=len(series)
        )
        X_fut = self._featurize(
            series, is_datetime, fut_exog, exog_cols, offset=len(series), n=steps
        )
        y_ctx = series.to_numpy(dtype=float)

        # Nori fits in-context (no gradient training); the cached model is
        # re-conditioned on each series' context rows before predicting.
        self._model.fit(X_ctx, y_ctx)

        if quantile_list is None:
            y_hat = self._model.predict(X_fut, output_type=self.point_estimate)
            predictions[name] = self._to_numpy(y_hat).reshape(-1, 1)
        else:
            q = self._to_numpy(
                self._model.predict(
                    X_fut, output_type="quantiles", quantiles=query_levels
                )
            )
            # Nori returns (n_quantiles, steps); skforecast expects
            # (steps, n_quantiles). Reorder columns to the requested order.
            q = q.reshape(len(query_levels), steps).T
            predictions[name] = q[:, column_for]

    return predictions

_load_model 

_load_model()

Load the NoriRegressor into self._model if not already set.

Returns:

Type Description
None

Raises:

Type Description
ImportError

If synthefy-nori is not installed.
Notes

The regressor is imported lazily from synthefy_nori and instantiated with nori_config. This method is a no-op when self._model is already populated (either by a prior call or by the model test-injection parameter). The same instance is reused across series and folds; it is re-conditioned per series via its in-context fit.

Source code in skforecast/foundation/_adapters.py 
3696
3697
3698
3699
3700
3701
3702
3703
3704
3705
3706
3707
3708
3709
3710
3711
3712
3713
3714
3715
3716
3717
3718
3719
3720
3721
3722
3723
3724
3725
3726
3727
3728
def _load_model(self) -> None:
    """
    Load the `NoriRegressor` into `self._model` if not already set.

    Returns
    -------
    None

    Raises
    ------
    ImportError
        If `synthefy-nori` is not installed.

    Notes
    -----
    The regressor is imported lazily from `synthefy_nori` and instantiated
    with `nori_config`. This method is a no-op when `self._model` is
    already populated (either by a prior call or by the `model`
    test-injection parameter). The same instance is reused across series
    and folds; it is re-conditioned per series via its in-context `fit`.
    """

    if self._model is not None:
        return
    try:
        from synthefy_nori import NoriRegressor
    except ImportError as exc:
        raise ImportError(
            "synthefy-nori is required for NoriAdapter. "
            "Install it with `pip install synthefy-nori`."
        ) from exc

    self._model = NoriRegressor(**self.nori_config)

_to_numpy staticmethod 

_to_numpy(values)

Convert a model output to a float numpy array.

Torch tensors (including those on a non-CPU device or requiring grad) are detached, moved to CPU, and converted before casting to float.

Parameters:

Name Type Description Default
values array - like

Model output, either a numpy array or a torch tensor.

 required

Returns:

Name Type Description
array numpy ndarray

Float numpy array.
Source code in skforecast/foundation/_adapters.py 
3730
3731
3732
3733
3734
3735
3736
3737
3738
3739
3740
3741
3742
3743
3744
3745
3746
3747
3748
3749
3750
3751
3752
3753
@staticmethod
def _to_numpy(values: Any) -> np.ndarray:
    """
    Convert a model output to a float numpy array.

    Torch tensors (including those on a non-CPU device or requiring grad)
    are detached, moved to CPU, and converted before casting to `float`.

    Parameters
    ----------
    values : array-like
        Model output, either a numpy array or a torch tensor.

    Returns
    -------
    array : numpy ndarray
        Float numpy array.

    """

    if hasattr(values, "detach"):
        values = values.detach().cpu().numpy()

    return np.asarray(values, dtype=float)

_known_future_columns staticmethod 

_known_future_columns(ctx_exog, fut_exog)

Return covariate columns present in both context and future exog.

Only known-future covariates (columns available over both the historical context and the forecast horizon) are usable by Nori.

Parameters:

Name Type Description Default
ctx_exog pandas DataFrame, pandas Series, or None

Historical (past) covariates for a single series.

 required
fut_exog pandas DataFrame, pandas Series, or None

Future covariates for a single series.

 required

Returns:

Name Type Description
columns list

Column names present in both ctx_exog and fut_exog, in the order they appear in ctx_exog. Empty when either input is None.
Source code in skforecast/foundation/_adapters.py 
3755
3756
3757
3758
3759
3760
3761
3762
3763
3764
3765
3766
3767
3768
3769
3770
3771
3772
3773
3774
3775
3776
3777
3778
3779
3780
3781
3782
3783
3784
3785
3786
3787
3788
3789
3790
3791
3792
3793
3794
3795
@staticmethod
def _known_future_columns(
    ctx_exog: pd.DataFrame | pd.Series | None,
    fut_exog: pd.DataFrame | pd.Series | None,
) -> list:
    """
    Return covariate columns present in both context and future exog.

    Only known-future covariates (columns available over both the
    historical context and the forecast horizon) are usable by Nori.

    Parameters
    ----------
    ctx_exog : pandas DataFrame, pandas Series, or None
        Historical (past) covariates for a single series.
    fut_exog : pandas DataFrame, pandas Series, or None
        Future covariates for a single series.

    Returns
    -------
    columns : list
        Column names present in both `ctx_exog` and `fut_exog`, in the
        order they appear in `ctx_exog`. Empty when either input is `None`.

    """

    if ctx_exog is None or fut_exog is None:
        return []
    c = (
        ctx_exog.columns
        if isinstance(ctx_exog, pd.DataFrame)
        else pd.Index([ctx_exog.name])
    )
    f = (
        fut_exog.columns
        if isinstance(fut_exog, pd.DataFrame)
        else pd.Index([fut_exog.name])
    )
    f_set = set(f)

    return [col for col in c if col in f_set]

_featurize 

_featurize(
    series, is_datetime, exog_block, exog_cols, offset, n
)

Build the tabular feature matrix for n rows starting at offset.

Features are a running index, optional calendar features and Fourier seasonal harmonics (datetime series) or Fourier(index) terms (RangeIndex series), and the known-future covariate columns.

Parameters:

Name Type Description Default
series pandas Series

The context series (used for its index and length).

 required
is_datetime bool

Whether the series has a DatetimeIndex.

 required
exog_block pandas DataFrame, pandas Series, or None

Covariate values for the rows being featurized (context_exog for the context block, exog for the horizon block).

 required
exog_cols list

Known-future covariate column names to include.

 required
offset int

Row offset of the block (0 for the context, len(series) for the forecast horizon).

 required
n int

Number of rows to featurize.

 required

Returns:

Name Type Description
X numpy ndarray

2-D float32 feature matrix of shape (n, n_features).
Source code in skforecast/foundation/_adapters.py 
3797
3798
3799
3800
3801
3802
3803
3804
3805
3806
3807
3808
3809
3810
3811
3812
3813
3814
3815
3816
3817
3818
3819
3820
3821
3822
3823
3824
3825
3826
3827
3828
3829
3830
3831
3832
3833
3834
3835
3836
3837
3838
3839
3840
3841
3842
3843
3844
3845
3846
3847
3848
3849
3850
3851
3852
3853
3854
3855
3856
3857
3858
3859
3860
3861
3862
3863
3864
3865
3866
3867
3868
3869
3870
3871
3872
3873
3874
3875
3876
3877
3878
3879
3880
3881
def _featurize(
    self,
    series: pd.Series,
    is_datetime: bool,
    exog_block: pd.DataFrame | pd.Series | None,
    exog_cols: list,
    offset: int,
    n: int,
) -> np.ndarray:
    """
    Build the tabular feature matrix for `n` rows starting at `offset`.

    Features are a running index, optional calendar features and Fourier
    seasonal harmonics (datetime series) or Fourier(index) terms
    (`RangeIndex` series), and the known-future covariate columns.

    Parameters
    ----------
    series : pandas Series
        The context series (used for its index and length).
    is_datetime : bool
        Whether the series has a `DatetimeIndex`.
    exog_block : pandas DataFrame, pandas Series, or None
        Covariate values for the rows being featurized (`context_exog` for
        the context block, `exog` for the horizon block).
    exog_cols : list
        Known-future covariate column names to include.
    offset : int
        Row offset of the block (`0` for the context, `len(series)` for the
        forecast horizon).
    n : int
        Number of rows to featurize.

    Returns
    -------
    X : numpy ndarray
        2-D `float32` feature matrix of shape `(n, n_features)`.

    """

    idx = np.arange(offset, offset + n, dtype=float)
    feats = [idx]  # running index

    if is_datetime and (self.add_calendar_features or self.n_fourier_terms > 0):
        ts = self._timestamps(series, offset, n)
        if self.add_calendar_features:
            feats += [
                ts.month.to_numpy(dtype=float),
                ts.day.to_numpy(dtype=float),
                ts.dayofweek.to_numpy(dtype=float),
                ts.dayofyear.to_numpy(dtype=float),
                ts.quarter.to_numpy(dtype=float),
                ts.hour.to_numpy(dtype=float),
            ]
        doy = ts.dayofyear.to_numpy(dtype=float)
        dow = ts.dayofweek.to_numpy(dtype=float)
        for k in range(1, self.n_fourier_terms + 1):
            feats += [
                np.sin(2 * np.pi * k * doy / 365.25),
                np.cos(2 * np.pi * k * doy / 365.25),
                np.sin(2 * np.pi * k * dow / 7.0),
                np.cos(2 * np.pi * k * dow / 7.0),
            ]
    elif self.n_fourier_terms > 0:
        period = max(len(series), 1)
        for k in range(1, self.n_fourier_terms + 1):
            feats += [
                np.sin(2 * np.pi * k * idx / period),
                np.cos(2 * np.pi * k * idx / period),
            ]

    X = np.column_stack(feats)

    if exog_cols and exog_block is not None:
        block = (
            exog_block.to_frame()
            if isinstance(exog_block, pd.Series)
            else exog_block
        )
        exog_values = np.column_stack(
            [self._to_float_array(block[col]) for col in exog_cols]
        )
        X = np.column_stack([X, exog_values])

    return X.astype(np.float32)

_to_float_array staticmethod 

_to_float_array(col_data)

Convert a numeric or boolean covariate column to a float32 array.

Parameters:

Name Type Description Default
col_data pandas Series

A single covariate column.

 required

Returns:

Name Type Description
col_array numpy ndarray

1-D float32 array.

Raises:

Type Description
ValueError

If the column is neither numeric nor boolean. Nori conditions only on numeric covariates; categoricals must be encoded as numbers.
Source code in skforecast/foundation/_adapters.py 
3883
3884
3885
3886
3887
3888
3889
3890
3891
3892
3893
3894
3895
3896
3897
3898
3899
3900
3901
3902
3903
3904
3905
3906
3907
3908
3909
3910
3911
3912
3913
@staticmethod
def _to_float_array(col_data: pd.Series) -> np.ndarray:
    """
    Convert a numeric or boolean covariate column to a `float32` array.

    Parameters
    ----------
    col_data : pandas Series
        A single covariate column.

    Returns
    -------
    col_array : numpy ndarray
        1-D `float32` array.

    Raises
    ------
    ValueError
        If the column is neither numeric nor boolean. Nori conditions only
        on numeric covariates; categoricals must be encoded as numbers.

    """

    if pd.api.types.is_numeric_dtype(col_data) or pd.api.types.is_bool_dtype(col_data):
        return col_data.astype(np.float32).to_numpy()

    raise ValueError(
        f"NoriAdapter supports only numeric covariates. Column "
        f"{col_data.name!r} has dtype {col_data.dtype}. Encode categorical "
        f"covariates as numeric values before passing them."
    )

_timestamps 

_timestamps(series, offset, n)

Return datetime timestamps for n rows starting at offset.

For the context block (offset == 0) the series' own index is returned. For the forecast horizon the index is extended by n steps at the series' frequency (inferred when not set).

Parameters:

Name Type Description Default
series pandas Series

The context series (used to determine the end timestamp and frequency).

 required
offset int

Row offset of the block. 0 selects the context index; any other value selects the extended forecast-horizon index.

 required
n int

Number of timestamps to return.

 required

Returns:

Name Type Description
timestamps pandas DatetimeIndex

Datetime timestamps for the requested block.
Source code in skforecast/foundation/_adapters.py 
3915
3916
3917
3918
3919
3920
3921
3922
3923
3924
3925
3926
3927
3928
3929
3930
3931
3932
3933
3934
3935
3936
3937
3938
3939
3940
3941
3942
3943
3944
3945
3946
def _timestamps(
    self, series: pd.Series, offset: int, n: int
) -> pd.DatetimeIndex:
    """
    Return datetime timestamps for `n` rows starting at `offset`.

    For the context block (`offset == 0`) the series' own index is
    returned. For the forecast horizon the index is extended by `n` steps
    at the series' frequency (inferred when not set).

    Parameters
    ----------
    series : pandas Series
        The context series (used to determine the end timestamp and
        frequency).
    offset : int
        Row offset of the block. `0` selects the context index; any other
        value selects the extended forecast-horizon index.
    n : int
        Number of timestamps to return.

    Returns
    -------
    timestamps : pandas DatetimeIndex
        Datetime timestamps for the requested block.

    """

    if offset == 0:
        return series.index

    return expand_index(series.index, steps=n)

skforecast.foundation._adapters.TSICLAdapter 

TSICLAdapter(
    model_id,
    *,
    model=None,
    checkpoint_version="tsicl-v1.ckpt",
    context_length=4096,
    device="auto",
    allow_auto_download=True
)

Adapter for EDF Lab TS-ICL foundation model.

Parameters:

Name Type Description Default
model_id str

Model ID, e.g. "taharnbl/TS-ICL". Used only to resolve this adapter; the underlying checkpoint is always downloaded from the taharnbl/TS-ICL Hugging Face repository, controlled by checkpoint_version.

 required
model object

Pre-instantiated TSICL model instance. If None, a new instance is created lazily on the first call to predict. Intended for testing only.

 None
checkpoint_version str

Checkpoint filename to download from the taharnbl/TS-ICL Hugging Face repository.

 'tsicl-v1.ckpt'
context_length int

Maximum number of historical observations to use as context. At fit time only the last context_length observations are stored. At predict time, if context is longer than context_length it is trimmed to this length; if it is shorter, all available observations are used as-is. Must be a positive integer.

 4096
device str

Device placement for inference. "auto" selects the best available accelerator (CUDA > MPS > CPU). Also accepts explicit values such as "cuda", "mps", or "cpu". Note that TS-ICL currently falls back to CPU whenever CUDA is unavailable, so "mps" has no effect on Apple Silicon.

 'auto'
allow_auto_download bool

Whether to allow automatic download of the checkpoint from Hugging Face Hub if it is not already cached locally.

 True

Attributes:

Name Type Description
model_id str

Model ID.
context_ dict

Stored training series after fitting.
context_exog_ dict

Stored historical exogenous variables after fitting.
checkpoint_version str

Checkpoint filename downloaded from Hugging Face Hub.
context_length int

Maximum number of historical observations used as context.
device str

Device placement for inference.
allow_auto_download bool

Whether automatic checkpoint download is allowed.
is_fitted bool

Whether the adapter has been fitted.
Notes

TS-ICL conditions on covariates that are known over the context, the forecast horizon, or both. skforecast exogenous variables map directly onto this channel: historical values (context_exog) are forwarded as past_covariates and future values (exog) as future_covariates. Covariates must be numeric; encode categoricals as numbers before passing them.

TS-ICL only supports quantile levels on a 0.01 grid in [0.01, 0.99] (i.e. a subset of [0.01, 0.02, ..., 0.99]); requesting any other level raises a ValueError from the underlying library.

References

.. [1] https://github.com/EDF-Lab/ts-icl

.. [2] https://huggingface.co/taharnbl/TS-ICL

Initialise the adapter.

Parameters:

Name Type Description Default
model_id str

Model ID, e.g. "taharnbl/TS-ICL". Used only to resolve this adapter.

 required
model object

Pre-instantiated TSICL model instance. If None, a new instance is created lazily on the first call to predict.

 None
checkpoint_version str

Checkpoint filename to download from the taharnbl/TS-ICL Hugging Face repository.

 'tsicl-v1.ckpt'
context_length int

Maximum number of historical observations to retain as context. At fit time only the last context_length observations of series (and exog) are stored. At predict time, if context is longer than context_length it is trimmed to this length before inference; if it is shorter, all available observations are passed as-is. Must be a positive integer.

 4096
device str

Device placement for inference. "auto" selects the best available accelerator (CUDA > MPS > CPU).

 'auto'
allow_auto_download bool

Whether to allow automatic download of the checkpoint from Hugging Face Hub if it is not already cached locally.

 True

Methods:

Name Description
get_params

Return the adapter's constructor parameters.
set_params

Set adapter parameters. Resets the model when checkpoint_version
fit

Store the training series and optional historical exogenous variables.
predict

Generate predictions using the TS-ICL model.
Source code in skforecast/foundation/_adapters.py 
2965
2966
2967
2968
2969
2970
2971
2972
2973
2974
2975
2976
2977
2978
2979
2980
2981
2982
2983
2984
2985
2986
2987
2988
2989
2990
2991
2992
2993
2994
2995
2996
2997
2998
2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
def __init__(
    self,
    model_id: str,
    *,
    model: Any | None = None,
    checkpoint_version: str = "tsicl-v1.ckpt",
    context_length: int = 4096,
    device: str = "auto",
    allow_auto_download: bool = True,
) -> None:
    """
    Initialise the adapter.

    Parameters
    ----------
    model_id : str
        Model ID, e.g. `"taharnbl/TS-ICL"`. Used only to resolve this
        adapter.
    model : object, default None
        Pre-instantiated `TSICL` model instance. If `None`, a new
        instance is created lazily on the first call to `predict`.
    checkpoint_version : str, default 'tsicl-v1.ckpt'
        Checkpoint filename to download from the `taharnbl/TS-ICL`
        Hugging Face repository.
    context_length : int, default 4096
        Maximum number of historical observations to retain as context.
        At `fit` time only the last `context_length` observations of
        `series` (and `exog`) are stored. At `predict` time, if
        `context` is longer than `context_length` it is trimmed to
        this length before inference; if it is shorter, all available
        observations are passed as-is. Must be a positive integer.
    device : str, default 'auto'
        Device placement for inference. `"auto"` selects the best
        available accelerator (CUDA > MPS > CPU).
    allow_auto_download : bool, default True
        Whether to allow automatic download of the checkpoint from
        Hugging Face Hub if it is not already cached locally.

    """

    _validate_positive_int("context_length", context_length)

    self.model_id             = model_id
    self._model               = model
    self._resolved_device     = None
    self.context_             = None
    self.context_exog_        = None
    self.checkpoint_version   = checkpoint_version
    self.context_length       = context_length
    self.device               = device
    self.allow_auto_download  = allow_auto_download
    self.is_fitted            = False

Attributes

allow_exog class-attribute instance-attribute 

allow_exog = True

model_id instance-attribute 

model_id = model_id

_model instance-attribute 

_model = model

_resolved_device instance-attribute 

_resolved_device = None

context_ instance-attribute 

context_ = None

context_exog_ instance-attribute 

context_exog_ = None

checkpoint_version instance-attribute 

checkpoint_version = checkpoint_version

context_length instance-attribute 

context_length = context_length

device instance-attribute 

device = device

allow_auto_download instance-attribute 

allow_auto_download = allow_auto_download

is_fitted instance-attribute 

is_fitted = False

Methods:

get_params 

get_params()

Return the adapter's constructor parameters.

Returns:

Name Type Description
params dict

Keys: model_id, checkpoint_version, context_length, device, allow_auto_download.
Source code in skforecast/foundation/_adapters.py 
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032
3033
3034
3035
def get_params(self) -> dict:
    """
    Return the adapter's constructor parameters.

    Returns
    -------
    params : dict
        Keys: `model_id`, `checkpoint_version`, `context_length`,
        `device`, `allow_auto_download`.

    """
    return {
        'model_id':             self.model_id,
        'checkpoint_version':   self.checkpoint_version,
        'context_length':       self.context_length,
        'device':               self.device,
        'allow_auto_download':  self.allow_auto_download,
    }

set_params 

set_params(**params)

Set adapter parameters. Resets the model when checkpoint_version or allow_auto_download changes, since those control which checkpoint is loaded.

Parameters:

Name Type Description Default
**params

Valid keys: model_id, checkpoint_version, context_length, device, allow_auto_download.

 {}

Returns:

Name Type Description
self TSICLAdapter
Source code in skforecast/foundation/_adapters.py 
3037
3038
3039
3040
3041
3042
3043
3044
3045
3046
3047
3048
3049
3050
3051
3052
3053
3054
3055
3056
3057
3058
3059
3060
3061
3062
3063
3064
3065
3066
3067
3068
3069
3070
def set_params(self, **params) -> TSICLAdapter:
    """
    Set adapter parameters. Resets the model when `checkpoint_version`
    or `allow_auto_download` changes, since those control which
    checkpoint is loaded.

    Parameters
    ----------
    **params :
        Valid keys: `model_id`, `checkpoint_version`, `context_length`,
        `device`, `allow_auto_download`.

    Returns
    -------
    self : TSICLAdapter

    """

    def validate(p: dict) -> dict:
        if "context_length" in p:
            _validate_positive_int("context_length", p["context_length"])
        return p

    return _apply_set_params(
        self, params,
        validate=validate,
        resets=(
            (
                {"checkpoint_version", "allow_auto_download"},
                lambda: setattr(self, "_model", None),
            ),
            ({"device"}, lambda: setattr(self, "_resolved_device", None)),
        ),
    )

fit 

fit(context, context_exog)

Store the training series and optional historical exogenous variables. No model training occurs since TS-ICL is a zero-shot inference model.

All input normalization and validation is performed upstream by FoundationModel; this method receives canonical dicts only.

Parameters:

Name Type Description Default
context dict pandas Series

Normalized training series, one entry per series.

 required
context_exog dict pandas DataFrame, pandas Series, or None

Per-series historical exogenous variables (past covariates).

 required

Returns:

Name Type Description
self TSICLAdapter
Source code in skforecast/foundation/_adapters.py 
3072
3073
3074
3075
3076
3077
3078
3079
3080
3081
3082
3083
3084
3085
3086
3087
3088
3089
3090
3091
3092
3093
3094
3095
3096
3097
3098
3099
3100
3101
def fit(
    self,
    context: dict[str, pd.Series],
    context_exog: dict[str, pd.DataFrame | pd.Series | None],
) -> TSICLAdapter:
    """
    Store the training series and optional historical exogenous variables.
    No model training occurs since TS-ICL is a zero-shot inference model.

    All input normalization and validation is performed upstream by
    `FoundationModel`; this method receives canonical dicts only.

    Parameters
    ----------
    context : dict pandas Series
        Normalized training series, one entry per series.
    context_exog : dict pandas DataFrame, pandas Series, or None
        Per-series historical exogenous variables (past covariates).

    Returns
    -------
    self : TSICLAdapter

    """

    self.context_ = context
    self.context_exog_ = context_exog
    self.is_fitted = True

    return self

predict 

predict(steps, context, context_exog, exog, quantiles)

Generate predictions using the TS-ICL model.

All input normalization, validation, and context trimming is performed upstream by FoundationModel; this method receives pre-processed dicts only.

Parameters:

Name Type Description Default
steps int

Number of steps ahead to forecast.

 required
context dict

Per-series context windows (already trimmed to context_length).

 required
context_exog dict

Per-series past covariates (already trimmed).

 required
exog dict

Per-series future covariates for the forecast horizon.

 required
quantiles list of float or None

Quantile levels to return. If None, a point forecast (median, quantile 0.5) is produced.

 required

Returns:

Name Type Description
predictions dict

Keys are series names. Each value is a 2-D array of shape (steps, n_quantiles).
Source code in skforecast/foundation/_adapters.py 
3103
3104
3105
3106
3107
3108
3109
3110
3111
3112
3113
3114
3115
3116
3117
3118
3119
3120
3121
3122
3123
3124
3125
3126
3127
3128
3129
3130
3131
3132
3133
3134
3135
3136
3137
3138
3139
3140
3141
3142
3143
3144
3145
3146
3147
3148
3149
3150
3151
3152
3153
3154
3155
3156
3157
3158
3159
3160
3161
3162
3163
3164
3165
3166
3167
3168
3169
3170
3171
3172
3173
3174
3175
3176
3177
3178
3179
def predict(
    self,
    steps: int,
    context: dict[str, pd.Series],
    context_exog: dict[str, pd.DataFrame | pd.Series | None],
    exog: dict[str, pd.DataFrame | pd.Series | None],
    quantiles: list[float] | tuple[float] | None
) -> dict[str, np.ndarray]:
    """
    Generate predictions using the TS-ICL model.

    All input normalization, validation, and context trimming is
    performed upstream by `FoundationModel`; this method receives
    pre-processed dicts only.

    Parameters
    ----------
    steps : int
        Number of steps ahead to forecast.
    context : dict
        Per-series context windows (already trimmed to
        `context_length`).
    context_exog : dict
        Per-series past covariates (already trimmed).
    exog : dict
        Per-series future covariates for the forecast horizon.
    quantiles : list of float or None
        Quantile levels to return. If `None`, a point forecast
        (median, quantile 0.5) is produced.

    Returns
    -------
    predictions : dict
        Keys are series names. Each value is a 2-D array of shape
        `(steps, n_quantiles)`.

    """

    # NOTE: the model is loaded lazily here so that the adapter can be
    # instantiated and fitted without requiring tsicl to be installed.
    self._load_model()

    import torch

    quantile_list = list(quantiles) if quantiles is not None else None
    query_levels = quantile_list if quantile_list is not None else [0.5]

    series_names_in = list(context.keys())
    inputs_list = [
        self._build_tsicl_input(
            context      = context[name].to_numpy(),
            context_exog = context_exog[name] if context_exog is not None else None,
            exog         = exog[name] if exog is not None else None,
        )
        for name in series_names_in
    ]

    if self._resolved_device is None:
        self._resolved_device = _resolve_torch_device(self.device)
    device = torch.device(self._resolved_device)

    _, quantile_preds = self._model.forecast(
        inputs            = inputs_list,
        prediction_length = steps,
        quantile_levels   = query_levels,
        context_length    = self.context_length,
        device            = device,
        denormalize       = True,
        squeeze_output    = False,
    )

    predictions: dict[str, np.ndarray] = {}
    for i, name in enumerate(series_names_in):
        q_arr = _tensor_to_numpy(quantile_preds[i])
        predictions[name] = q_arr[0]  # drop the single-variate dim

    return predictions

_load_model 

_load_model()

Load the TS-ICL model into self._model if not already set.

Returns:

Type Description
None

Raises:

Type Description
ImportError

If tsicl is not installed.
Notes

The model is imported lazily from tsicl and instantiated via TSICL(checkpoint_version=..., allow_auto_download=...), which downloads (or loads from cache) the checkpoint from the taharnbl/TS-ICL Hugging Face repository. This method is a no-op when self._model is already populated.

Source code in skforecast/foundation/_adapters.py 
3181
3182
3183
3184
3185
3186
3187
3188
3189
3190
3191
3192
3193
3194
3195
3196
3197
3198
3199
3200
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
def _load_model(self) -> None:
    """
    Load the TS-ICL model into `self._model` if not already set.

    Returns
    -------
    None

    Raises
    ------
    ImportError
        If `tsicl` is not installed.

    Notes
    -----
    The model is imported lazily from `tsicl` and instantiated via
    `TSICL(checkpoint_version=..., allow_auto_download=...)`, which
    downloads (or loads from cache) the checkpoint from the
    `taharnbl/TS-ICL` Hugging Face repository. This method is a no-op
    when `self._model` is already populated.

    """

    if self._model is not None:
        return
    try:
        from tsicl import TSICL
    except ImportError as exc:
        raise ImportError(
            "tsicl is required for TSICLAdapter. "
            "Install it with `pip install tsicl`."
        ) from exc

    self._model = TSICL(
        checkpoint_version   = self.checkpoint_version,
        allow_auto_download  = self.allow_auto_download,
    )

_to_covariate_array staticmethod 

_to_covariate_array(col_data)

Convert a covariate column to a float32 numpy array.

Parameters:

Name Type Description Default
col_data array - like

A single covariate column (e.g. a pandas Series or 1-D array).

 required

Returns:

Name Type Description
col_array numpy ndarray

A 1-D float32 numpy array.

Raises:

Type Description
ValueError

If the column is neither numeric nor boolean. TS-ICL only conditions on numeric covariates; categoricals must be encoded as numbers.
Source code in skforecast/foundation/_adapters.py 
3219
3220
3221
3222
3223
3224
3225
3226
3227
3228
3229
3230
3231
3232
3233
3234
3235
3236
3237
3238
3239
3240
3241
3242
3243
3244
3245
3246
3247
3248
3249
3250
3251
3252
3253
3254
3255
3256
3257
3258
3259
3260
@staticmethod
def _to_covariate_array(col_data: Any) -> np.ndarray:
    """
    Convert a covariate column to a `float32` numpy array.

    Parameters
    ----------
    col_data : array-like
        A single covariate column (e.g. a pandas Series or 1-D array).

    Returns
    -------
    col_array : numpy ndarray
        A 1-D `float32` numpy array.

    Raises
    ------
    ValueError
        If the column is neither numeric nor boolean. TS-ICL only
        conditions on numeric covariates; categoricals must be encoded
        as numbers.

    """

    if isinstance(col_data, pd.Series):
        if pd.api.types.is_numeric_dtype(col_data) or pd.api.types.is_bool_dtype(col_data):
            return col_data.astype(np.float32).to_numpy()
        raise ValueError(
            f"TSICLAdapter supports only numeric covariates. Column "
            f"{col_data.name!r} has dtype {col_data.dtype}. Encode "
            f"categorical covariates as numeric values before passing them."
        )

    arr = np.asarray(col_data)
    if arr.dtype.kind in ("i", "u", "f", "b"):  # integer, unsigned int, float, bool
        return arr.astype(np.float32)

    raise ValueError(
        f"TSICLAdapter supports only numeric covariates. Got array of "
        f"dtype {arr.dtype}. Encode categorical covariates as numeric "
        f"values before passing them."
    )

_build_tsicl_input 

_build_tsicl_input(context, context_exog=None, exog=None)

Build the input dict consumed by TSICL.forecast.

Parameters:

Name Type Description Default
context numpy ndarray

1-D array of observed time series values used as context. Must be castable to float32.

 required
context_exog pandas DataFrame, pandas Series

Historical exogenous variables whose index is aligned to context. Each column (or the single Series, referenced by its name) becomes an entry in the returned "past_covariates" dict. Must be numeric or boolean.

 None
exog pandas DataFrame, pandas Series

Future-known exogenous variables covering the forecast horizon. Must have exactly steps rows. Each column becomes an entry in the returned "future_covariates" dict. Must be numeric or boolean.

 None

Returns:

Name Type Description
input_dict dict

Dictionary with mandatory key "target" (1-D float32 numpy ndarray) and optional keys "past_covariates" and "future_covariates", each mapping column names to 1-D float32 arrays.
Source code in skforecast/foundation/_adapters.py 
3262
3263
3264
3265
3266
3267
3268
3269
3270
3271
3272
3273
3274
3275
3276
3277
3278
3279
3280
3281
3282
3283
3284
3285
3286
3287
3288
3289
3290
3291
3292
3293
3294
3295
3296
3297
3298
3299
3300
3301
3302
3303
3304
3305
3306
3307
3308
3309
3310
3311
3312
3313
3314
3315
3316
3317
def _build_tsicl_input(
    self,
    context: np.ndarray,
    context_exog: pd.DataFrame | pd.Series | None = None,
    exog: pd.DataFrame | pd.Series | None = None,
) -> dict[str, Any]:
    """
    Build the input dict consumed by `TSICL.forecast`.

    Parameters
    ----------
    context : numpy ndarray
        1-D array of observed time series values used as context. Must be
        castable to `float32`.
    context_exog : pandas DataFrame, pandas Series, default None
        Historical exogenous variables whose index is aligned to
        `context`. Each column (or the single Series, referenced by
        its name) becomes an entry in the returned "past_covariates"
        dict. Must be numeric or boolean.
    exog : pandas DataFrame, pandas Series, default None
        Future-known exogenous variables covering the forecast horizon.
        Must have exactly `steps` rows. Each column becomes an entry in
        the returned "future_covariates" dict. Must be numeric or
        boolean.

    Returns
    -------
    input_dict : dict
        Dictionary with mandatory key "target" (1-D `float32`
        `numpy ndarray`) and optional keys "past_covariates" and
        "future_covariates", each mapping column names to 1-D
        `float32` arrays.

    """

    input_dict = {"target": np.asarray(context, dtype=np.float32)}
    if context_exog is not None:
        df = (
            context_exog
            if isinstance(context_exog, pd.DataFrame)
            else context_exog.to_frame()
        )
        input_dict["past_covariates"] = {
            col: TSICLAdapter._to_covariate_array(df[col]) for col in df.columns
        }
    if exog is not None:
        df = (
            exog
            if isinstance(exog, pd.DataFrame)
            else exog.to_frame()
        )
        input_dict["future_covariates"] = {
            col: TSICLAdapter._to_covariate_array(df[col]) for col in df.columns
        }

    return input_dict