ForecasterAutoregDirect¶
ForecasterAutoregDirect (ForecasterBase)
¶
This class turns any regressor compatible with the scikit-learn API into a
autoregressive direct multi-step forecaster. A separate model is created for each forecast time step. See documentation for more details.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
regressor |
object |
An instance of a regressor or pipeline compatible with the scikit-learn API. |
required |
lags |
Union[int, numpy.ndarray, list] |
Lags used as predictors. Index starts at 1, so lag 1 is equal to t-1.
|
required |
steps |
int |
Maximum number of future steps the forecaster will predict when using
method |
required |
transformer_y |
Optional[object] |
An instance of a transformer (preprocessor) compatible with the scikit-learn
preprocessing API with methods: fit, transform, fit_transform and inverse_transform.
ColumnTransformers are not allowed since they do not have inverse_transform method.
The transformation is applied to |
None |
transformer_exog |
Optional[object] |
An instance of a transformer (preprocessor) compatible with the scikit-learn
preprocessing API. The transformation is applied to |
None |
weight_func |
Optional[Callable] |
Function that defines the individual weights for each sample based on the
index. For example, a function that assigns a lower weight to certain dates.
Ignored if |
None |
fit_kwargs |
Optional[dict] |
Additional arguments to be passed to the |
None |
forecaster_id |
Union[str, int] |
Name used as an identifier of the forecaster. |
None |
Attributes:
| Name | Type | Description |
|---|---|---|
regressor |
regressor or pipeline compatible with the scikit-learn API |
An instance of a regressor or pipeline compatible with the scikit-learn API.
An instance of this regressor is trained for each step. All of them
are stored in |
regressors_ |
dict |
Dictionary with regressors trained for each step. They are initialized
as a copy of |
steps |
int |
Number of future steps the forecaster will predict when using method
|
lags |
numpy ndarray |
Lags used as predictors. |
transformer_y |
object transformer (preprocessor), default `None` |
An instance of a transformer (preprocessor) compatible with the scikit-learn
preprocessing API with methods: fit, transform, fit_transform and inverse_transform.
ColumnTransformers are not allowed since they do not have inverse_transform method.
The transformation is applied to |
transformer_exog |
object transformer (preprocessor), default `None` |
An instance of a transformer (preprocessor) compatible with the scikit-learn
preprocessing API. The transformation is applied to |
weight_func |
Callable |
Function that defines the individual weights for each sample based on the
index. For example, a function that assigns a lower weight to certain dates.
Ignored if |
source_code_weight_func |
str |
Source code of the custom function used to create weights. New in version 0.6.0 |
max_lag |
int |
Maximum value of lag included in |
window_size |
int |
Size of the window needed to create the predictors. It is equal to
|
last_window |
pandas Series |
Last window the forecaster has seen during training. It stores the
values needed to predict the next |
index_type |
type |
Type of index of the input used in training. |
index_freq |
str |
Frequency of Index of the input used in training. |
training_range |
pandas Index |
First and last values of index of the data used during training. |
included_exog |
bool |
If the forecaster has been trained using exogenous variable/s. |
exog_type |
type |
Type of exogenous variable/s used in training. |
exog_dtypes |
dict |
Type of each exogenous variable/s used in training. If |
exog_col_names |
list |
Names of columns of |
X_train_col_names |
list |
Names of columns of the matrix created internally for training. |
fit_kwargs |
dict |
Additional arguments to be passed to the |
in_sample_residuals |
dict |
Residuals of the models when predicting training data. Only stored up to
1000 values per model in the form |
out_sample_residuals |
dict |
Residuals of the models when predicting non training data. Only stored
up to 1000 values per model in the form |
fitted |
bool |
Tag to identify if the regressor has been fitted (trained). |
creation_date |
str |
Date of creation. |
fit_date |
str |
Date of last fit. |
skforcast_version |
str |
Version of skforecast library used to create the forecaster. |
python_version |
str |
Version of python used to create the forecaster. |
forecaster_id |
str, int default `None` |
Name used as an identifier of the forecaster. |
fit_kwargs |
dict, default `None` |
Additional parameters passed to the |
Source code in skforecast/ForecasterAutoregDirect/ForecasterAutoregDirect.py
class ForecasterAutoregDirect(ForecasterBase):
"""
This class turns any regressor compatible with the scikit-learn API into a
autoregressive direct multi-step forecaster. A separate model is created for
each forecast time step. See documentation for more details.
Parameters
----------
regressor : regressor or pipeline compatible with the scikit-learn API
An instance of a regressor or pipeline compatible with the scikit-learn API.
lags : int, list, 1d numpy ndarray, range
Lags used as predictors. Index starts at 1, so lag 1 is equal to t-1.
`int`: include lags from 1 to `lags` (included).
`list`, `numpy ndarray` or range: include only lags present in `lags`.
steps : int
Maximum number of future steps the forecaster will predict when using
method `predict()`. Since a different model is created for each step,
this value should be defined before training.
transformer_y : object transformer (preprocessor), default `None`
An instance of a transformer (preprocessor) compatible with the scikit-learn
preprocessing API with methods: fit, transform, fit_transform and inverse_transform.
ColumnTransformers are not allowed since they do not have inverse_transform method.
The transformation is applied to `y` before training the forecaster.
transformer_exog : object transformer (preprocessor), default `None`
An instance of a transformer (preprocessor) compatible with the scikit-learn
preprocessing API. The transformation is applied to `exog` before training the
forecaster. `inverse_transform` is not available when using ColumnTransformers.
weight_func : Callable, default `None`
Function that defines the individual weights for each sample based on the
index. For example, a function that assigns a lower weight to certain dates.
Ignored if `regressor` does not have the argument `sample_weight` in its `fit`
method. The resulting `sample_weight` cannot have negative values.
**New in version 0.6.0**
fit_kwargs : dict, default `None`
Additional arguments to be passed to the `fit` method of the regressor.
**New in version 0.8.0**
forecaster_id : str, int, default `None`
Name used as an identifier of the forecaster.
Attributes
----------
regressor : regressor or pipeline compatible with the scikit-learn API
An instance of a regressor or pipeline compatible with the scikit-learn API.
An instance of this regressor is trained for each step. All of them
are stored in `self.regressors_`.
regressors_ : dict
Dictionary with regressors trained for each step. They are initialized
as a copy of `regressor`.
steps : int
Number of future steps the forecaster will predict when using method
`predict()`. Since a different model is created for each step, this value
should be defined before training.
lags : numpy ndarray
Lags used as predictors.
transformer_y : object transformer (preprocessor), default `None`
An instance of a transformer (preprocessor) compatible with the scikit-learn
preprocessing API with methods: fit, transform, fit_transform and inverse_transform.
ColumnTransformers are not allowed since they do not have inverse_transform method.
The transformation is applied to `y` before training the forecaster.
transformer_exog : object transformer (preprocessor), default `None`
An instance of a transformer (preprocessor) compatible with the scikit-learn
preprocessing API. The transformation is applied to `exog` before training the
forecaster. `inverse_transform` is not available when using ColumnTransformers.
weight_func : Callable
Function that defines the individual weights for each sample based on the
index. For example, a function that assigns a lower weight to certain dates.
Ignored if `regressor` does not have the argument `sample_weight` in its `fit`
method.
**New in version 0.6.0**
source_code_weight_func : str
Source code of the custom function used to create weights.
**New in version 0.6.0**
max_lag : int
Maximum value of lag included in `lags`.
window_size : int
Size of the window needed to create the predictors. It is equal to
`max_lag`.
last_window : pandas Series
Last window the forecaster has seen during training. It stores the
values needed to predict the next `step` immediately after the training data.
index_type : type
Type of index of the input used in training.
index_freq : str
Frequency of Index of the input used in training.
training_range : pandas Index
First and last values of index of the data used during training.
included_exog : bool
If the forecaster has been trained using exogenous variable/s.
exog_type : type
Type of exogenous variable/s used in training.
exog_dtypes : dict
Type of each exogenous variable/s used in training. If `transformer_exog`
is used, the dtypes are calculated after the transformation.
exog_col_names : list
Names of columns of `exog` if `exog` used in training was a pandas
DataFrame.
X_train_col_names : list
Names of columns of the matrix created internally for training.
fit_kwargs : dict
Additional arguments to be passed to the `fit` method of the regressor.
**New in version 0.8.0**
in_sample_residuals : dict
Residuals of the models when predicting training data. Only stored up to
1000 values per model in the form `{step: residuals}`. If `transformer_y`
is not `None`, residuals are stored in the transformed scale.
out_sample_residuals : dict
Residuals of the models when predicting non training data. Only stored
up to 1000 values per model in the form `{step: residuals}`. If `transformer_y`
is not `None`, residuals are assumed to be in the transformed scale. Use
`set_out_sample_residuals()` method to set values.
fitted : bool
Tag to identify if the regressor has been fitted (trained).
creation_date : str
Date of creation.
fit_date : str
Date of last fit.
skforcast_version : str
Version of skforecast library used to create the forecaster.
python_version : str
Version of python used to create the forecaster.
forecaster_id : str, int default `None`
Name used as an identifier of the forecaster.
fit_kwargs : dict, default `None`
Additional parameters passed to the `fit` method of the regressor.
Notes
-----
A separate model is created for each forecasting time step. It is important to
note that all models share the same parameter and hyperparameter configuration.
"""
def __init__(
self,
regressor: object,
steps: int,
lags: Union[int, np.ndarray, list],
transformer_y: Optional[object]=None,
transformer_exog: Optional[object]=None,
weight_func: Optional[Callable]=None,
fit_kwargs: Optional[dict]=None,
forecaster_id: Optional[Union[str, int]]=None,
) -> None:
self.regressor = regressor
self.steps = steps
self.transformer_y = transformer_y
self.transformer_exog = transformer_exog
self.weight_func = weight_func
self.source_code_weight_func = None
self.last_window = None
self.index_type = None
self.index_freq = None
self.training_range = None
self.included_exog = False
self.exog_type = None
self.exog_dtypes = None
self.exog_col_names = None
self.X_train_col_names = None
self.fitted = False
self.creation_date = pd.Timestamp.today().strftime('%Y-%m-%d %H:%M:%S')
self.fit_date = None
self.skforcast_version = skforecast.__version__
self.python_version = sys.version.split(" ")[0]
self.forecaster_id = forecaster_id
if not isinstance(steps, int):
raise TypeError(
(f"`steps` argument must be an int greater than or equal to 1. "
f"Got {type(steps)}.")
)
if steps < 1:
raise ValueError(
f"`steps` argument must be greater than or equal to 1. Got {steps}."
)
self.regressors_ = {step: clone(self.regressor) for step in range(1, steps + 1)}
self.lags = initialize_lags(type(self).__name__, lags)
self.max_lag = max(self.lags)
self.window_size = self.max_lag
self.weight_func, self.source_code_weight_func, _ = initialize_weights(
forecaster_name = type(self).__name__,
regressor = regressor,
weight_func = weight_func,
series_weights = None
)
self.fit_kwargs = check_select_fit_kwargs(
regressor = regressor,
fit_kwargs = fit_kwargs
)
self.in_sample_residuals = {step: None for step in range(1, steps + 1)}
self.out_sample_residuals = None
def __repr__(
self
) -> str:
"""
Information displayed when a ForecasterAutoregDirect object is printed.
"""
if isinstance(self.regressor, sklearn.pipeline.Pipeline):
name_pipe_steps = tuple(name + "__" for name in self.regressor.named_steps.keys())
params = {key : value for key, value in self.regressor.get_params().items() \
if key.startswith(name_pipe_steps)}
else:
params = self.regressor.get_params()
info = (
f"{'=' * len(type(self).__name__)} \n"
f"{type(self).__name__} \n"
f"{'=' * len(type(self).__name__)} \n"
f"Regressor: {self.regressor} \n"
f"Lags: {self.lags} \n"
f"Transformer for y: {self.transformer_y} \n"
f"Transformer for exog: {self.transformer_exog} \n"
f"Weight function included: {True if self.weight_func is not None else False} \n"
f"Window size: {self.window_size} \n"
f"Maximum steps predicted: {self.steps} \n"
f"Exogenous included: {self.included_exog} \n"
f"Type of exogenous variable: {self.exog_type} \n"
f"Exogenous variables names: {self.exog_col_names} \n"
f"Training range: {self.training_range.to_list() if self.fitted else None} \n"
f"Training index type: {str(self.index_type).split('.')[-1][:-2] if self.fitted else None} \n"
f"Training index frequency: {self.index_freq if self.fitted else None} \n"
f"Regressor parameters: {params} \n"
f"fit_kwargs: {self.fit_kwargs} \n"
f"Creation date: {self.creation_date} \n"
f"Last fit date: {self.fit_date} \n"
f"Skforecast version: {self.skforcast_version} \n"
f"Python version: {self.python_version} \n"
f"Forecaster id: {self.forecaster_id} \n"
)
return info
def _create_lags(
self,
y: np.ndarray
) -> Tuple[np.ndarray, np.ndarray]:
"""
Transforms a 1d array into a 2d array (X) and a 1d array (y). Each row
in X is associated with a value of y and it represents the lags that
precede it.
Notice that, the returned matrix X_data, contains the lag 1 in the first
column, the lag 2 in the second column and so on.
Parameters
----------
y : 1d numpy ndarray
Training time series.
Returns
-------
X_data : 2d numpy ndarray, shape (samples - max(self.lags), len(self.lags))
2d numpy array with the lagged values (predictors).
y_data : 1d numpy ndarray, shape (samples - max(self.lags),)
Values of the time series related to each row of `X_data`.
"""
n_splits = len(y) - self.max_lag - (self.steps - 1) # rows of y_data
if n_splits <= 0:
raise ValueError(
(f"The maximum lag ({self.max_lag}) must be less than the length "
f"of the series minus the number of steps ({len(y)-(self.steps-1)}).")
)
X_data = np.full(shape=(n_splits, len(self.lags)), fill_value=np.nan, dtype=float)
for i, lag in enumerate(self.lags):
X_data[:, i] = y[self.max_lag - lag : -(lag + self.steps - 1)]
y_data = np.full(shape=(n_splits, self.steps), fill_value=np.nan, dtype=float)
for step in range(self.steps):
y_data[:, step] = y[self.max_lag + step : self.max_lag + step + n_splits]
return X_data, y_data
def create_train_X_y(
self,
y: pd.Series,
exog: Optional[Union[pd.Series, pd.DataFrame]]=None
) -> Tuple[pd.DataFrame, pd.DataFrame]:
"""
Create training matrices from univariate time series and exogenous
variables. The resulting matrices contain the target variable and predictors
needed to train all the regressors (one per step).
Parameters
----------
y : pandas Series
Training time series.
exog : pandas Series, pandas DataFrame, default `None`
Exogenous variable/s included as predictor/s. Must have the same
number of observations as `y` and their indexes must be aligned.
Returns
-------
X_train : pandas DataFrame, shape (len(y) - self.max_lag, len(self.lags) + exog.shape[1]*steps)
Pandas DataFrame with the training values (predictors) for each step.
y_train : pandas DataFrame, shape (len(y) - self.max_lag, )
Values (target) of the time series related to each row of `X_train`
for each step.
"""
if len(y) < self.max_lag + self.steps:
raise ValueError(
(f"Minimum length of `y` for training this forecaster is "
f"{self.max_lag + self.steps}. Got {len(y)}. Reduce the "
f"number of predicted steps, {self.steps}, or the maximum "
f"lag, {self.max_lag}, if no more data is available.")
)
check_y(y=y)
y = transform_series(
series = y,
transformer = self.transformer_y,
fit = True,
inverse_transform = False
)
y_values, y_index = preprocess_y(y=y)
if exog is not None:
if len(exog) != len(y):
raise ValueError(
(f"`exog` must have same number of samples as `y`. "
f"length `exog`: ({len(exog)}), length `y`: ({len(y)})")
)
check_exog(exog=exog, allow_nan=True)
# Need here for filter_train_X_y_for_step to work without fitting
self.included_exog = True
if isinstance(exog, pd.Series):
exog = transform_series(
series = exog,
transformer = self.transformer_exog,
fit = True,
inverse_transform = False
)
else:
exog = transform_dataframe(
df = exog,
transformer = self.transformer_exog,
fit = True,
inverse_transform = False
)
check_exog(exog=exog, allow_nan=False)
check_exog_dtypes(exog)
self.exog_dtypes = get_exog_dtypes(exog=exog)
_, exog_index = preprocess_exog(exog=exog, return_values=False)
if not (exog_index[:len(y_index)] == y_index).all():
raise ValueError(
("Different index for `y` and `exog`. They must be equal "
"to ensure the correct alignment of values.")
)
X_train, y_train = self._create_lags(y=y_values)
X_train_col_names = [f"lag_{i}" for i in self.lags]
X_train = pd.DataFrame(
data = X_train,
columns = X_train_col_names,
index = y_index[self.max_lag + (self.steps -1): ]
)
if exog is not None:
# Transform exog to match direct format
# The first `self.max_lag` positions have to be removed from X_exog
# since they are not in X_lags.
exog_to_train = exog_to_direct(exog=exog, steps=self.steps).iloc[-X_train.shape[0]:, :]
X_train = pd.concat((X_train, exog_to_train), axis=1)
self.X_train_col_names = X_train.columns.to_list()
y_train_col_names = [f"y_step_{i+1}" for i in range(self.steps)]
y_train = pd.DataFrame(
data = y_train,
index = y_index[self.max_lag + (self.steps -1): ],
columns = y_train_col_names,
)
return X_train, y_train
def filter_train_X_y_for_step(
self,
step: int,
X_train: pd.DataFrame,
y_train: pd.Series,
remove_suffix: bool=False
) -> Tuple[pd.DataFrame, pd.Series]:
"""
Select the columns needed to train a forecaster for a specific step.
The input matrices should be created using `create_train_X_y()`. If
`remove_suffix=True` the suffix "_step_i" will be removed from the
column names.
Parameters
----------
step : int
Step for which columns must be selected selected. Starts at 1.
X_train : pandas DataFrame
Pandas DataFrame with the training values (predictors).
y_train : pandas Series
Values (target) of the time series related to each row of `X_train`.
remove_suffix : bool, default `False`
If True, suffix "_step_i" is removed from the column names.
Returns
-------
X_train_step : pandas DataFrame
Pandas DataFrame with the training values (predictors) for step.
y_train_step : pandas Series, shape (len(y) - self.max_lag)
Values (target) of the time series related to each row of `X_train`.
"""
if (step < 1) or (step > self.steps):
raise ValueError(
(f"Invalid value `step`. For this forecaster, minimum value is 1 "
f"and the maximum step is {self.steps}.")
)
step = step - 1 # Matrices X_train and y_train start at index 0.
y_train_step = y_train.iloc[:, step]
if not self.included_exog:
X_train_step = X_train
else:
idx_columns_lags = np.arange(len(self.lags))
idx_columns_exog = np.arange(X_train.shape[1])[len(self.lags) + step::self.steps]
idx_columns = np.hstack((idx_columns_lags, idx_columns_exog))
X_train_step = X_train.iloc[:, idx_columns]
if remove_suffix:
X_train_step.columns = [col_name.replace(f"_step_{step + 1}", "")
for col_name in X_train_step.columns]
y_train_step.name = y_train_step.name.replace(f"_step_{step + 1}", "")
return X_train_step, y_train_step
def create_sample_weights(
self,
X_train: pd.DataFrame,
)-> np.ndarray:
"""
Crate weights for each observation according to the forecaster's attribute
`weight_func`.
Parameters
----------
X_train : pandas DataFrame
Dataframe generated with the methods `create_train_X_y` and
`filter_train_X_y_for_step`, first return.
Returns
-------
sample_weight : numpy ndarray
Weights to use in `fit` method.
"""
sample_weight = None
if self.weight_func is not None:
sample_weight = self.weight_func(X_train.index)
if sample_weight is not None:
if np.isnan(sample_weight).any():
raise ValueError(
"The resulting `sample_weight` cannot have NaN values."
)
if np.any(sample_weight < 0):
raise ValueError(
"The resulting `sample_weight` cannot have negative values."
)
if np.sum(sample_weight) == 0:
raise ValueError(
("The resulting `sample_weight` cannot be normalized because "
"the sum of the weights is zero.")
)
return sample_weight
def fit(
self,
y: pd.Series,
exog: Optional[Union[pd.Series, pd.DataFrame]]=None
) -> None:
"""
Training Forecaster.
Additional arguments to be passed to the `fit` method of the regressor
can be added with the `fit_kwargs` argument when initializing the forecaster.
Parameters
----------
y : pandas Series
Training time series.
exog : pandas Series, pandas DataFrame, default `None`
Exogenous variable/s included as predictor/s. Must have the same
number of observations as `y` and their indexes must be aligned so
that y[i] is regressed on exog[i].
Returns
-------
None
"""
# Reset values in case the forecaster has already been fitted.
self.index_type = None
self.index_freq = None
self.last_window = None
self.included_exog = False
self.exog_type = None
self.exog_dtypes = None
self.exog_col_names = None
self.X_train_col_names = None
self.in_sample_residuals = {step: None for step in range(1, self.steps + 1)}
self.fitted = False
self.training_range = None
if exog is not None:
self.included_exog = True
self.exog_type = type(exog)
self.exog_col_names = \
exog.columns.to_list() if isinstance(exog, pd.DataFrame) else exog.name
X_train, y_train = self.create_train_X_y(y=y, exog=exog)
# Train one regressor for each step
for step in range(1, self.steps + 1):
# self.regressors_ and self.filter_train_X_y_for_step expect
# first step to start at value 1
X_train_step, y_train_step = self.filter_train_X_y_for_step(
step = step,
X_train = X_train,
y_train = y_train,
remove_suffix = True
)
sample_weight = self.create_sample_weights(X_train=X_train_step)
if sample_weight is not None:
self.regressors_[step].fit(
X = X_train_step,
y = y_train_step,
sample_weight = sample_weight,
**self.fit_kwargs
)
else:
self.regressors_[step].fit(
X = X_train_step,
y = y_train_step,
**self.fit_kwargs
)
residuals = (y_train_step - self.regressors_[step].predict(X_train_step)).to_numpy()
if len(residuals) > 1000:
# Only up to 1000 residuals are stored
rng = np.random.default_rng(seed=123)
residuals = rng.choice(
a = residuals,
size = 1000,
replace = False
)
self.in_sample_residuals[step] = residuals
self.fitted = True
self.fit_date = pd.Timestamp.today().strftime('%Y-%m-%d %H:%M:%S')
self.training_range = preprocess_y(y=y, return_values=False)[1][[0, -1]]
self.index_type = type(X_train.index)
if isinstance(X_train.index, pd.DatetimeIndex):
self.index_freq = X_train.index.freqstr
else:
self.index_freq = X_train.index.step
self.last_window = y.iloc[-self.max_lag:].copy()
def predict(
self,
steps: Optional[Union[int, list]]=None,
last_window: Optional[pd.Series]=None,
exog: Optional[Union[pd.Series, pd.DataFrame]]=None
) -> pd.Series:
"""
Predict n steps ahead.
Parameters
----------
steps : int, list, None, default `None`
Predict n steps. The value of `steps` must be less than or equal to the
value of steps defined when initializing the forecaster. Starts at 1.
If `int`:
Only steps within the range of 1 to int are predicted.
If `list`:
List of ints. Only the steps contained in the list are predicted.
If `None`:
As many steps are predicted as were defined at initialization.
last_window : pandas Series, default `None`
Series values used to create the predictors (lags) needed in the
first iteration of the prediction (t + 1).
If `last_window = None`, the values stored in` self.last_window` are
used to calculate the initial predictors, and the predictions start
right after training data.
exog : pandas Series, pandas DataFrame, default `None`
Exogenous variable/s included as predictor/s.
Returns
-------
predictions : pandas Series
Predicted values.
"""
if isinstance(steps, int):
steps = list(np.arange(steps) + 1)
elif steps is None:
steps = list(np.arange(self.steps) + 1)
elif isinstance(steps, list):
steps = list(np.array(steps))
for step in steps:
if not isinstance(step, (int, np.int64, np.int32)):
raise TypeError(
(f"`steps` argument must be an int, a list of ints or `None`. "
f"Got {type(steps)}.")
)
if last_window is None:
last_window = copy(self.last_window)
check_predict_input(
forecaster_name = type(self).__name__,
steps = steps,
fitted = self.fitted,
included_exog = self.included_exog,
index_type = self.index_type,
index_freq = self.index_freq,
window_size = self.window_size,
last_window = last_window,
last_window_exog = None,
exog = exog,
exog_type = self.exog_type,
exog_col_names = self.exog_col_names,
interval = None,
alpha = None,
max_steps = self.steps,
levels = None,
series_col_names = None
)
if exog is not None:
if isinstance(exog, pd.DataFrame):
exog = transform_dataframe(
df = exog,
transformer = self.transformer_exog,
fit = False,
inverse_transform = False
)
else:
exog = transform_series(
series = exog,
transformer = self.transformer_exog,
fit = False,
inverse_transform = False
)
check_exog_dtypes(exog=exog)
exog_values = exog_to_direct(exog=exog.iloc[:max(steps), ], steps=max(steps)).to_numpy()
else:
exog_values = None
last_window = transform_series(
series = last_window,
transformer = self.transformer_y,
fit = False,
inverse_transform = False
)
last_window_values, last_window_index = preprocess_last_window(
last_window = last_window
)
X_lags = last_window_values[-self.lags].reshape(1, -1)
if exog is None:
Xs = [X_lags] * len(steps)
else:
Xs = [
np.hstack([X_lags, exog_values[0][step-1::max(steps)].reshape(1, -1)])
for step in steps
]
regressors = [self.regressors_[step] for step in steps]
with warnings.catch_warnings():
# Suppress scikit-learn warning: "X does not have valid feature names,
# but NoOpTransformer was fitted with feature names".
warnings.simplefilter("ignore")
predictions = [
regressor.predict(X)[0] for regressor, X in zip(regressors, Xs)
]
idx = expand_index(index=last_window_index, steps=max(steps))
predictions = pd.Series(
data = predictions,
index = idx[np.array(steps)-1],
name = 'pred'
)
predictions = transform_series(
series = predictions,
transformer = self.transformer_y,
fit = False,
inverse_transform = True
)
return predictions
def predict_pandas(
self,
steps: Optional[Union[int, list]]=None,
last_window: Optional[pd.Series]=None,
exog: Optional[Union[pd.Series, pd.DataFrame]]=None
) -> pd.Series: # pragma: no cover
"""
Equivalent to predict() but using pandas instead of numpy.
"""
if isinstance(steps, int):
steps = list(np.arange(steps) + 1)
elif steps is None:
steps = list(np.arange(self.steps) + 1)
elif isinstance(steps, list):
steps = list(np.array(steps))
for step in steps:
if not isinstance(step, (int, np.int64, np.int32)):
raise TypeError(
(f"`steps` argument must be an int, a list of ints or `None`. "
f"Got {type(steps)}.")
)
if last_window is None:
last_window = copy(self.last_window)
_, last_window_index = preprocess_last_window(
last_window = last_window,
return_values = False
)
idx = expand_index(index=last_window_index, steps=max(steps))
X_lags = last_window.iloc[-self.lags]
X_lags.index = [f"lag_{lag}" for lag in self.lags]
X_lags = X_lags.to_frame().T
if exog is None:
Xs = [X_lags] * len(steps)
else:
Xs = [
pd.concat([X_lags, exog.iloc[step-1::max(steps)]], axis=1)
for step in steps
]
regressors = [self.regressors_[step] for step in steps]
predictions = [regressor.predict(X)[0] for regressor, X in zip(regressors, Xs)]
predictions = pd.Series(
data = predictions,
index = idx[np.array(steps)-1],
name = 'pred',
)
return predictions
def predict_bootstrapping(
self,
steps: Optional[Union[int, list]]=None,
last_window: Optional[pd.Series]=None,
exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
n_boot: int=500,
random_state: int=123,
in_sample_residuals: bool=True
) -> pd.DataFrame:
"""
Generate multiple forecasting predictions using a bootstrapping process.
By sampling from a collection of past observed errors (the residuals),
each iteration of bootstrapping generates a different set of predictions.
See the Notes section for more information.
Parameters
----------
steps : int, list, None, default `None`
Predict n steps. The value of `steps` must be less than or equal to the
value of steps defined when initializing the forecaster. Starts at 1.
If `int`:
Only steps within the range of 1 to int are predicted.
If `list`:
List of ints. Only the steps contained in the list are predicted.
If `None`:
As many steps are predicted as were defined at initialization.
last_window : pandas Series, default `None`
Series values used to create the predictors (lags) needed in the
first iteration of the prediction (t + 1).
If `last_window = None`, the values stored in` self.last_window` are
used to calculate the initial predictors, and the predictions start
right after training data.
exog : pandas Series, pandas DataFrame, default `None`
Exogenous variable/s included as predictor/s.
n_boot : int, default `500`
Number of bootstrapping iterations used to estimate prediction
intervals.
random_state : int, default `123`
Sets a seed to the random generator, so that boot intervals are always
deterministic.
in_sample_residuals : bool, default `True`
If `True`, residuals from the training data are used as proxy of
prediction error to create prediction intervals. If `False`, out of
sample residuals are used. In the latter case, the user should have
calculated and stored the residuals within the forecaster (see
`set_out_sample_residuals()`).
Returns
-------
boot_predictions : pandas DataFrame, shape (steps, n_boot)
Predictions generated by bootstrapping.
Notes
-----
More information about prediction intervals in forecasting:
https://otexts.com/fpp3/prediction-intervals.html#prediction-intervals-from-bootstrapped-residuals
Forecasting: Principles and Practice (3nd ed) Rob J Hyndman and George Athanasopoulos.
"""
if isinstance(steps, int):
steps = list(np.arange(steps) + 1)
elif steps is None:
steps = list(np.arange(self.steps) + 1)
elif isinstance(steps, list):
steps = list(np.array(steps))
if in_sample_residuals:
if not set(steps).issubset(set(self.in_sample_residuals.keys())):
raise ValueError(
(f"Not `forecaster.in_sample_residuals` for steps: "
f"{set(steps) - set(self.in_sample_residuals.keys())}.")
)
residuals = self.in_sample_residuals
else:
if self.out_sample_residuals is None:
raise ValueError(
("`forecaster.out_sample_residuals` is `None`. Use "
"`in_sample_residuals=True` or method `set_out_sample_residuals()` "
"before `predict_interval()`, `predict_bootstrapping()` or "
"`predict_dist()`.")
)
else:
if not set(steps).issubset(set(self.out_sample_residuals.keys())):
raise ValueError(
(f"Not `forecaster.out_sample_residuals` for steps: "
f"{set(steps) - set(self.out_sample_residuals.keys())}. "
f"Use method `set_out_sample_residuals()`.")
)
residuals = self.out_sample_residuals
check_residuals = "forecaster.in_sample_residuals" if in_sample_residuals else "forecaster.out_sample_residuals"
for step in steps:
if residuals[step] is None:
raise ValueError(
(f"forecaster residuals for step {step} are `None`. Check {check_residuals}.")
)
elif (residuals[step] == None).any():
raise ValueError(
(f"forecaster residuals for step {step} contains `None` values. Check {check_residuals}.")
)
predictions = self.predict(
steps = steps,
last_window = last_window,
exog = exog
)
# Predictions must be in the transformed scale before adding residuals
predictions = transform_series(
series = predictions,
transformer = self.transformer_y,
fit = False,
inverse_transform = False
)
boot_predictions = pd.concat([predictions] * n_boot, axis=1)
boot_predictions.columns= [f"pred_boot_{i}" for i in range(n_boot)]
rng = np.random.default_rng(seed=random_state)
for i, step in enumerate(steps):
sample_residuals = rng.choice(
a = residuals[step],
size = n_boot,
replace = True
)
boot_predictions.iloc[i, :] = boot_predictions.iloc[i, :] + sample_residuals
if self.transformer_y:
for col in boot_predictions.columns:
boot_predictions[col] = transform_series(
series = boot_predictions[col],
transformer = self.transformer_y,
fit = False,
inverse_transform = True
)
return boot_predictions
def predict_interval(
self,
steps: Optional[Union[int, list]]=None,
last_window: Optional[pd.Series]=None,
exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
interval: list=[5, 95],
n_boot: int=500,
random_state: int=123,
in_sample_residuals: bool=True
) -> pd.DataFrame:
"""
Bootstrapping based prediction intervals.
Both predictions and intervals are returned.
Parameters
----------
steps : int, list, None, default `None`
Predict n steps. The value of `steps` must be less than or equal to the
value of steps defined when initializing the forecaster. Starts at 1.
If `int`:
Only steps within the range of 1 to int are predicted.
If `list`:
List of ints. Only the steps contained in the list are predicted.
If `None`:
As many steps are predicted as were defined at initialization.
last_window : pandas Series, default `None`
Series values used to create the predictors (lags) needed in the
first iteration of the prediction (t + 1).
If `last_window = None`, the values stored in` self.last_window` are
used to calculate the initial predictors, and the predictions start
right after training data.
exog : pandas Series, pandas DataFrame, default `None`
Exogenous variable/s included as predictor/s.
interval : list, default `[5, 95]`
Confidence of the prediction interval estimated. Sequence of
percentiles to compute, which must be between 0 and 100 inclusive.
For example, interval of 95% should be as `interval = [2.5, 97.5]`.
n_boot : int, default `500`
Number of bootstrapping iterations used to estimate prediction
intervals.
random_state : int, default `123`
Sets a seed to the random generator, so that boot intervals are always
deterministic.
in_sample_residuals : bool, default `True`
If `True`, residuals from the training data are used as proxy of
prediction error to create prediction intervals. If `False`, out of
sample residuals are used. In the latter case, the user should have
calculated and stored the residuals within the forecaster (see
`set_out_sample_residuals()`).
Returns
-------
predictions : pandas DataFrame
Values predicted by the forecaster and their estimated interval:
- pred: predictions.
- lower_bound: lower bound of the interval.
- upper_bound: upper bound interval of the interval.
Notes
-----
More information about prediction intervals in forecasting:
https://otexts.com/fpp2/prediction-intervals.html
Forecasting: Principles and Practice (2nd ed) Rob J Hyndman and
George Athanasopoulos.
"""
check_interval(interval=interval)
predictions = self.predict(
steps = steps,
last_window = last_window,
exog = exog
)
boot_predictions = self.predict_bootstrapping(
steps = steps,
last_window = last_window,
exog = exog,
n_boot = n_boot,
random_state = random_state,
in_sample_residuals = in_sample_residuals
)
interval = np.array(interval)/100
predictions_interval = boot_predictions.quantile(q=interval, axis=1).transpose()
predictions_interval.columns = ['lower_bound', 'upper_bound']
predictions = pd.concat((predictions, predictions_interval), axis=1)
return predictions
def predict_dist(
self,
distribution: object,
steps: Optional[Union[int, list]]=None,
last_window: Optional[pd.Series]=None,
exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
n_boot: int=500,
random_state: int=123,
in_sample_residuals: bool=True
) -> pd.DataFrame:
"""
Fit a given probability distribution for each step. After generating
multiple forecasting predictions through a bootstrapping process, each
step is fitted to the given distribution.
Parameters
----------
distribution : Object
A distribution object from scipy.stats.
steps : int, list, None, default `None`
Predict n steps. The value of `steps` must be less than or equal to the
value of steps defined when initializing the forecaster. Starts at 1.
If `int`:
Only steps within the range of 1 to int are predicted.
If `list`:
List of ints. Only the steps contained in the list are predicted.
If `None`:
As many steps are predicted as were defined at initialization.
last_window : pandas Series, default `None`
Series values used to create the predictors (lags) needed in the
first iteration of the prediction (t + 1).
If `last_window = None`, the values stored in` self.last_window` are
used to calculate the initial predictors, and the predictions start
right after training data.
exog : pandas Series, pandas DataFrame, default `None`
Exogenous variable/s included as predictor/s.
n_boot : int, default `500`
Number of bootstrapping iterations used to estimate prediction
intervals.
random_state : int, default `123`
Sets a seed to the random generator, so that boot intervals are always
deterministic.
in_sample_residuals : bool, default `True`
If `True`, residuals from the training data are used as proxy of
prediction error to create prediction intervals. If `False`, out of
sample residuals are used. In the latter case, the user should have
calculated and stored the residuals within the forecaster (see
`set_out_sample_residuals()`).
Returns
-------
predictions : pandas DataFrame
Distribution parameters estimated for each step.
"""
boot_samples = self.predict_bootstrapping(
steps = steps,
last_window = last_window,
exog = exog,
n_boot = n_boot,
random_state = random_state,
in_sample_residuals = in_sample_residuals
)
param_names = [p for p in inspect.signature(distribution._pdf).parameters if not p=='x'] + ["loc","scale"]
param_values = np.apply_along_axis(lambda x: distribution.fit(x), axis=1, arr=boot_samples)
predictions = pd.DataFrame(
data = param_values,
columns = param_names,
index = boot_samples.index
)
return predictions
def set_params(
self,
params: dict
) -> None:
"""
Set new values to the parameters of the scikit learn model stored in the
forecaster. It is important to note that all models share the same
configuration of parameters and hyperparameters.
Parameters
----------
params : dict
Parameters values.
Returns
-------
self
"""
self.regressor = clone(self.regressor)
self.regressor.set_params(**params)
self.regressors_ = {step: clone(self.regressor) for step in range(1, self.steps + 1)}
def set_fit_kwargs(
self,
fit_kwargs: dict
) -> None:
"""
Set new values for the additional keyword arguments passed to the `fit`
method of the regressor.
Parameters
----------
fit_kwargs : dict
Dict of the form {"argument": new_value}.
Returns
-------
None
"""
self.fit_kwargs = check_select_fit_kwargs(self.regressor, fit_kwargs=fit_kwargs)
def set_lags(
self,
lags: Union[int, list, np.ndarray, range]
) -> None:
"""
Set new value to the attribute `lags`.
Attributes `max_lag` and `window_size` are also updated.
Parameters
----------
lags : int, list, 1D np.ndarray, range
Lags used as predictors. Index starts at 1, so lag 1 is equal to t-1.
`int`: include lags from 1 to `lags`.
`list` or `np.ndarray`: include only lags present in `lags`.
Returns
-------
None
"""
self.lags = initialize_lags(type(self).__name__, lags)
self.max_lag = max(self.lags)
self.window_size = max(self.lags)
def set_out_sample_residuals(
self,
residuals: dict,
append: bool=True,
transform: bool=True,
random_state: int=123
)-> None:
"""
Set new values to the attribute `out_sample_residuals`. Out of sample
residuals are meant to be calculated using observations that did not
participate in the training process.
Parameters
----------
residuals : dict
Dictionary of numpy ndarrays with the residuals of each model in the
form {step: residuals}. If len(residuals) > 1000, only a random
sample of 1000 values are stored.
append : bool, default `True`
If `True`, new residuals are added to the once already stored in the
attribute `out_sample_residuals`. Once the limit of 1000 values is
reached, no more values are appended. If False, `out_sample_residuals`
is overwritten with the new residuals.
transform : bool, default `True`
If `True`, new residuals are transformed using self.transformer_y.
random_state : int, default `123`
Sets a seed to the random sampling for reproducible output.
Returns
-------
self
"""
if not isinstance(residuals, dict) or not all(isinstance(x, np.ndarray) for x in residuals.values()):
raise TypeError(
f"`residuals` argument must be a dict of numpy ndarrays in the form "
"`{step: residuals}`. "
f"Got {type(residuals)}."
)
if not self.fitted:
raise sklearn.exceptions.NotFittedError(
("This forecaster is not fitted yet. Call `fit` with appropriate "
"arguments before using `set_out_sample_residuals()`.")
)
if self.out_sample_residuals is None:
self.out_sample_residuals = {step: None for step in range(1, self.steps + 1)}
if not set(self.out_sample_residuals.keys()).issubset(set(residuals.keys())):
warnings.warn(
f"""
Only residuals of models (steps)
{set(self.out_sample_residuals.keys()).intersection(set(residuals.keys()))}
are updated.
"""
)
residuals = {key: value for key, value in residuals.items() if key in self.out_sample_residuals.keys()}
if not transform and self.transformer_y is not None:
warnings.warn(
(f"Argument `transform` is set to `False` but forecaster was trained "
f"using a transformer {self.transformer_y}. Ensure that the new residuals "
f"are already transformed or set `transform=True`.")
)
if transform and self.transformer_y is not None:
warnings.warn(
(f"Residuals will be transformed using the same transformer used "
f"when training the forecaster ({self.transformer_y}). Ensure the "
f"new residuals are on the same scale as the original time series.")
)
for key, value in residuals.items():
residuals[key] = transform_series(
series = pd.Series(value, name='residuals'),
transformer = self.transformer_y,
fit = False,
inverse_transform = False
).to_numpy()
for key, value in residuals.items():
if len(value) > 1000:
rng = np.random.default_rng(seed=random_state)
value = rng.choice(a=value, size=1000, replace=False)
if append and self.out_sample_residuals[key] is not None:
free_space = max(0, 1000 - len(self.out_sample_residuals[key]))
if len(value) < free_space:
value = np.hstack((
self.out_sample_residuals[key],
value
))
else:
value = np.hstack((
self.out_sample_residuals[key],
value[:free_space]
))
self.out_sample_residuals[key] = value
def get_feature_importances(
self,
step: int
) -> pd.DataFrame:
"""
Return impurity-based feature importance of the model stored in
the forecaster for a specific step. Since a separate model is created for
each forecast time step, it is necessary to select the model from which
retrieve information. Only valid when regressor stores internally the
feature importances in the attribute `feature_importances_` or `coef_`.
Parameters
----------
step : int
Model from which retrieve information (a separate model is created
for each forecast time step). First step is 1.
Returns
-------
feature_importances : pandas DataFrame
Feature importances associated with each predictor.
"""
if not isinstance(step, int):
raise TypeError(
f'`step` must be an integer. Got {type(step)}.'
)
if not self.fitted:
raise sklearn.exceptions.NotFittedError(
("This forecaster is not fitted yet. Call `fit` with appropriate "
"arguments before using `get_feature_importances()`.")
)
if (step < 1) or (step > self.steps):
raise ValueError(
(f"The step must have a value from 1 to the maximum number of steps "
f"({self.steps}). Got {step}.")
)
if isinstance(self.regressor, sklearn.pipeline.Pipeline):
estimator = self.regressors_[step][-1]
else:
estimator = self.regressors_[step]
idx_columns_lags = np.arange(len(self.lags))
if self.included_exog:
idx_columns_exog = np.arange(len(self.X_train_col_names))[len(self.lags) + step-1::self.steps]
else:
idx_columns_exog = np.array([], dtype=int)
idx_columns = np.hstack((idx_columns_lags, idx_columns_exog))
feature_names = [self.X_train_col_names[i].replace(f"_step_{step}", "")
for i in idx_columns]
if hasattr(estimator, 'feature_importances_'):
feature_importances = estimator.feature_importances_
elif hasattr(estimator, 'coef_'):
feature_importances = estimator.coef_
else:
warnings.warn(
(f"Impossible to access feature importances for regressor of type "
f"{type(estimator)}. This method is only valid when the "
f"regressor stores internally the feature importances in the "
f"attribute `feature_importances_` or `coef_`.")
)
feature_importances = None
if feature_importances is not None:
feature_importances = pd.DataFrame({
'feature': feature_names,
'importance': feature_importances
})
return feature_importances
def get_feature_importance(
self,
step: int
) -> pd.DataFrame:
"""
This method has been replaced by `get_feature_importances()`.
Return impurity-based feature importance of the model stored in
the forecaster for a specific step. Since a separate model is created for
each forecast time step, it is necessary to select the model from which
retrieve information. Only valid when regressor stores internally the
feature importances in the attribute `feature_importances_` or `coef_`.
Parameters
----------
step : int
Model from which retrieve information (a separate model is created
for each forecast time step). First step is 1.
Returns
-------
feature_importances : pandas DataFrame
Feature importances associated with each predictor.
"""
warnings.warn(
("get_feature_importance() method has been renamed to get_feature_importances()."
"This method will be removed in skforecast 0.9.0.")
)
return self.get_feature_importances(step=step)
create_sample_weights(self, X_train)
¶
Crate weights for each observation according to the forecaster's attribute
weight_func.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
X_train |
DataFrame |
Dataframe generated with the methods |
required |
Returns:
| Type | Description |
|---|---|
ndarray |
Weights to use in |
Source code in skforecast/ForecasterAutoregDirect/ForecasterAutoregDirect.py
def create_sample_weights(
self,
X_train: pd.DataFrame,
)-> np.ndarray:
"""
Crate weights for each observation according to the forecaster's attribute
`weight_func`.
Parameters
----------
X_train : pandas DataFrame
Dataframe generated with the methods `create_train_X_y` and
`filter_train_X_y_for_step`, first return.
Returns
-------
sample_weight : numpy ndarray
Weights to use in `fit` method.
"""
sample_weight = None
if self.weight_func is not None:
sample_weight = self.weight_func(X_train.index)
if sample_weight is not None:
if np.isnan(sample_weight).any():
raise ValueError(
"The resulting `sample_weight` cannot have NaN values."
)
if np.any(sample_weight < 0):
raise ValueError(
"The resulting `sample_weight` cannot have negative values."
)
if np.sum(sample_weight) == 0:
raise ValueError(
("The resulting `sample_weight` cannot be normalized because "
"the sum of the weights is zero.")
)
return sample_weight
create_train_X_y(self, y, exog=None)
¶
Create training matrices from univariate time series and exogenous
variables. The resulting matrices contain the target variable and predictors needed to train all the regressors (one per step).
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
y |
Series |
Training time series. |
required |
exog |
Union[pandas.core.series.Series, pandas.core.frame.DataFrame] |
Exogenous variable/s included as predictor/s. Must have the same
number of observations as |
None |
Returns:
| Type | Description |
|---|---|
Tuple[pandas.core.frame.DataFrame, pandas.core.frame.DataFrame] |
Pandas DataFrame with the training values (predictors) for each step. |
Source code in skforecast/ForecasterAutoregDirect/ForecasterAutoregDirect.py
def create_train_X_y(
self,
y: pd.Series,
exog: Optional[Union[pd.Series, pd.DataFrame]]=None
) -> Tuple[pd.DataFrame, pd.DataFrame]:
"""
Create training matrices from univariate time series and exogenous
variables. The resulting matrices contain the target variable and predictors
needed to train all the regressors (one per step).
Parameters
----------
y : pandas Series
Training time series.
exog : pandas Series, pandas DataFrame, default `None`
Exogenous variable/s included as predictor/s. Must have the same
number of observations as `y` and their indexes must be aligned.
Returns
-------
X_train : pandas DataFrame, shape (len(y) - self.max_lag, len(self.lags) + exog.shape[1]*steps)
Pandas DataFrame with the training values (predictors) for each step.
y_train : pandas DataFrame, shape (len(y) - self.max_lag, )
Values (target) of the time series related to each row of `X_train`
for each step.
"""
if len(y) < self.max_lag + self.steps:
raise ValueError(
(f"Minimum length of `y` for training this forecaster is "
f"{self.max_lag + self.steps}. Got {len(y)}. Reduce the "
f"number of predicted steps, {self.steps}, or the maximum "
f"lag, {self.max_lag}, if no more data is available.")
)
check_y(y=y)
y = transform_series(
series = y,
transformer = self.transformer_y,
fit = True,
inverse_transform = False
)
y_values, y_index = preprocess_y(y=y)
if exog is not None:
if len(exog) != len(y):
raise ValueError(
(f"`exog` must have same number of samples as `y`. "
f"length `exog`: ({len(exog)}), length `y`: ({len(y)})")
)
check_exog(exog=exog, allow_nan=True)
# Need here for filter_train_X_y_for_step to work without fitting
self.included_exog = True
if isinstance(exog, pd.Series):
exog = transform_series(
series = exog,
transformer = self.transformer_exog,
fit = True,
inverse_transform = False
)
else:
exog = transform_dataframe(
df = exog,
transformer = self.transformer_exog,
fit = True,
inverse_transform = False
)
check_exog(exog=exog, allow_nan=False)
check_exog_dtypes(exog)
self.exog_dtypes = get_exog_dtypes(exog=exog)
_, exog_index = preprocess_exog(exog=exog, return_values=False)
if not (exog_index[:len(y_index)] == y_index).all():
raise ValueError(
("Different index for `y` and `exog`. They must be equal "
"to ensure the correct alignment of values.")
)
X_train, y_train = self._create_lags(y=y_values)
X_train_col_names = [f"lag_{i}" for i in self.lags]
X_train = pd.DataFrame(
data = X_train,
columns = X_train_col_names,
index = y_index[self.max_lag + (self.steps -1): ]
)
if exog is not None:
# Transform exog to match direct format
# The first `self.max_lag` positions have to be removed from X_exog
# since they are not in X_lags.
exog_to_train = exog_to_direct(exog=exog, steps=self.steps).iloc[-X_train.shape[0]:, :]
X_train = pd.concat((X_train, exog_to_train), axis=1)
self.X_train_col_names = X_train.columns.to_list()
y_train_col_names = [f"y_step_{i+1}" for i in range(self.steps)]
y_train = pd.DataFrame(
data = y_train,
index = y_index[self.max_lag + (self.steps -1): ],
columns = y_train_col_names,
)
return X_train, y_train
filter_train_X_y_for_step(self, step, X_train, y_train, remove_suffix=False)
¶
Select the columns needed to train a forecaster for a specific step.
The input matrices should be created using create_train_X_y(). If
remove_suffix=True the suffix "_step_i" will be removed from the
column names.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
step |
int |
Step for which columns must be selected selected. Starts at 1. |
required |
X_train |
DataFrame |
Pandas DataFrame with the training values (predictors). |
required |
y_train |
Series |
Values (target) of the time series related to each row of |
required |
remove_suffix |
bool |
If True, suffix "_step_i" is removed from the column names. |
False |
Returns:
| Type | Description |
|---|---|
Tuple[pandas.core.frame.DataFrame, pandas.core.series.Series] |
Pandas DataFrame with the training values (predictors) for step. |
Source code in skforecast/ForecasterAutoregDirect/ForecasterAutoregDirect.py
def filter_train_X_y_for_step(
self,
step: int,
X_train: pd.DataFrame,
y_train: pd.Series,
remove_suffix: bool=False
) -> Tuple[pd.DataFrame, pd.Series]:
"""
Select the columns needed to train a forecaster for a specific step.
The input matrices should be created using `create_train_X_y()`. If
`remove_suffix=True` the suffix "_step_i" will be removed from the
column names.
Parameters
----------
step : int
Step for which columns must be selected selected. Starts at 1.
X_train : pandas DataFrame
Pandas DataFrame with the training values (predictors).
y_train : pandas Series
Values (target) of the time series related to each row of `X_train`.
remove_suffix : bool, default `False`
If True, suffix "_step_i" is removed from the column names.
Returns
-------
X_train_step : pandas DataFrame
Pandas DataFrame with the training values (predictors) for step.
y_train_step : pandas Series, shape (len(y) - self.max_lag)
Values (target) of the time series related to each row of `X_train`.
"""
if (step < 1) or (step > self.steps):
raise ValueError(
(f"Invalid value `step`. For this forecaster, minimum value is 1 "
f"and the maximum step is {self.steps}.")
)
step = step - 1 # Matrices X_train and y_train start at index 0.
y_train_step = y_train.iloc[:, step]
if not self.included_exog:
X_train_step = X_train
else:
idx_columns_lags = np.arange(len(self.lags))
idx_columns_exog = np.arange(X_train.shape[1])[len(self.lags) + step::self.steps]
idx_columns = np.hstack((idx_columns_lags, idx_columns_exog))
X_train_step = X_train.iloc[:, idx_columns]
if remove_suffix:
X_train_step.columns = [col_name.replace(f"_step_{step + 1}", "")
for col_name in X_train_step.columns]
y_train_step.name = y_train_step.name.replace(f"_step_{step + 1}", "")
return X_train_step, y_train_step
fit(self, y, exog=None)
¶
Training Forecaster.
Additional arguments to be passed to the fit method of the regressor
can be added with the fit_kwargs argument when initializing the forecaster.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
y |
Series |
Training time series. |
required |
exog |
Union[pandas.core.series.Series, pandas.core.frame.DataFrame] |
Exogenous variable/s included as predictor/s. Must have the same
number of observations as |
None |
Source code in skforecast/ForecasterAutoregDirect/ForecasterAutoregDirect.py
def fit(
self,
y: pd.Series,
exog: Optional[Union[pd.Series, pd.DataFrame]]=None
) -> None:
"""
Training Forecaster.
Additional arguments to be passed to the `fit` method of the regressor
can be added with the `fit_kwargs` argument when initializing the forecaster.
Parameters
----------
y : pandas Series
Training time series.
exog : pandas Series, pandas DataFrame, default `None`
Exogenous variable/s included as predictor/s. Must have the same
number of observations as `y` and their indexes must be aligned so
that y[i] is regressed on exog[i].
Returns
-------
None
"""
# Reset values in case the forecaster has already been fitted.
self.index_type = None
self.index_freq = None
self.last_window = None
self.included_exog = False
self.exog_type = None
self.exog_dtypes = None
self.exog_col_names = None
self.X_train_col_names = None
self.in_sample_residuals = {step: None for step in range(1, self.steps + 1)}
self.fitted = False
self.training_range = None
if exog is not None:
self.included_exog = True
self.exog_type = type(exog)
self.exog_col_names = \
exog.columns.to_list() if isinstance(exog, pd.DataFrame) else exog.name
X_train, y_train = self.create_train_X_y(y=y, exog=exog)
# Train one regressor for each step
for step in range(1, self.steps + 1):
# self.regressors_ and self.filter_train_X_y_for_step expect
# first step to start at value 1
X_train_step, y_train_step = self.filter_train_X_y_for_step(
step = step,
X_train = X_train,
y_train = y_train,
remove_suffix = True
)
sample_weight = self.create_sample_weights(X_train=X_train_step)
if sample_weight is not None:
self.regressors_[step].fit(
X = X_train_step,
y = y_train_step,
sample_weight = sample_weight,
**self.fit_kwargs
)
else:
self.regressors_[step].fit(
X = X_train_step,
y = y_train_step,
**self.fit_kwargs
)
residuals = (y_train_step - self.regressors_[step].predict(X_train_step)).to_numpy()
if len(residuals) > 1000:
# Only up to 1000 residuals are stored
rng = np.random.default_rng(seed=123)
residuals = rng.choice(
a = residuals,
size = 1000,
replace = False
)
self.in_sample_residuals[step] = residuals
self.fitted = True
self.fit_date = pd.Timestamp.today().strftime('%Y-%m-%d %H:%M:%S')
self.training_range = preprocess_y(y=y, return_values=False)[1][[0, -1]]
self.index_type = type(X_train.index)
if isinstance(X_train.index, pd.DatetimeIndex):
self.index_freq = X_train.index.freqstr
else:
self.index_freq = X_train.index.step
self.last_window = y.iloc[-self.max_lag:].copy()
get_feature_importance(self, step)
¶
This method has been replaced by get_feature_importances().
Return impurity-based feature importance of the model stored in
the forecaster for a specific step. Since a separate model is created for
each forecast time step, it is necessary to select the model from which
retrieve information. Only valid when regressor stores internally the
feature importances in the attribute feature_importances_ or coef_.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
step |
int |
Model from which retrieve information (a separate model is created for each forecast time step). First step is 1. |
required |
Returns:
| Type | Description |
|---|---|
DataFrame |
Feature importances associated with each predictor. |
Source code in skforecast/ForecasterAutoregDirect/ForecasterAutoregDirect.py
def get_feature_importance(
self,
step: int
) -> pd.DataFrame:
"""
This method has been replaced by `get_feature_importances()`.
Return impurity-based feature importance of the model stored in
the forecaster for a specific step. Since a separate model is created for
each forecast time step, it is necessary to select the model from which
retrieve information. Only valid when regressor stores internally the
feature importances in the attribute `feature_importances_` or `coef_`.
Parameters
----------
step : int
Model from which retrieve information (a separate model is created
for each forecast time step). First step is 1.
Returns
-------
feature_importances : pandas DataFrame
Feature importances associated with each predictor.
"""
warnings.warn(
("get_feature_importance() method has been renamed to get_feature_importances()."
"This method will be removed in skforecast 0.9.0.")
)
return self.get_feature_importances(step=step)
get_feature_importances(self, step)
¶
Return impurity-based feature importance of the model stored in
the forecaster for a specific step. Since a separate model is created for
each forecast time step, it is necessary to select the model from which
retrieve information. Only valid when regressor stores internally the
feature importances in the attribute feature_importances_ or coef_.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
step |
int |
Model from which retrieve information (a separate model is created for each forecast time step). First step is 1. |
required |
Returns:
| Type | Description |
|---|---|
DataFrame |
Feature importances associated with each predictor. |
Source code in skforecast/ForecasterAutoregDirect/ForecasterAutoregDirect.py
def get_feature_importances(
self,
step: int
) -> pd.DataFrame:
"""
Return impurity-based feature importance of the model stored in
the forecaster for a specific step. Since a separate model is created for
each forecast time step, it is necessary to select the model from which
retrieve information. Only valid when regressor stores internally the
feature importances in the attribute `feature_importances_` or `coef_`.
Parameters
----------
step : int
Model from which retrieve information (a separate model is created
for each forecast time step). First step is 1.
Returns
-------
feature_importances : pandas DataFrame
Feature importances associated with each predictor.
"""
if not isinstance(step, int):
raise TypeError(
f'`step` must be an integer. Got {type(step)}.'
)
if not self.fitted:
raise sklearn.exceptions.NotFittedError(
("This forecaster is not fitted yet. Call `fit` with appropriate "
"arguments before using `get_feature_importances()`.")
)
if (step < 1) or (step > self.steps):
raise ValueError(
(f"The step must have a value from 1 to the maximum number of steps "
f"({self.steps}). Got {step}.")
)
if isinstance(self.regressor, sklearn.pipeline.Pipeline):
estimator = self.regressors_[step][-1]
else:
estimator = self.regressors_[step]
idx_columns_lags = np.arange(len(self.lags))
if self.included_exog:
idx_columns_exog = np.arange(len(self.X_train_col_names))[len(self.lags) + step-1::self.steps]
else:
idx_columns_exog = np.array([], dtype=int)
idx_columns = np.hstack((idx_columns_lags, idx_columns_exog))
feature_names = [self.X_train_col_names[i].replace(f"_step_{step}", "")
for i in idx_columns]
if hasattr(estimator, 'feature_importances_'):
feature_importances = estimator.feature_importances_
elif hasattr(estimator, 'coef_'):
feature_importances = estimator.coef_
else:
warnings.warn(
(f"Impossible to access feature importances for regressor of type "
f"{type(estimator)}. This method is only valid when the "
f"regressor stores internally the feature importances in the "
f"attribute `feature_importances_` or `coef_`.")
)
feature_importances = None
if feature_importances is not None:
feature_importances = pd.DataFrame({
'feature': feature_names,
'importance': feature_importances
})
return feature_importances
predict(self, steps=None, last_window=None, exog=None)
¶
Predict n steps ahead.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
steps |
Union[int, list] |
Predict n steps. The value of If If If |
None |
last_window |
Optional[pandas.core.series.Series] |
Series values used to create the predictors (lags) needed in the first iteration of the prediction (t + 1). If |
None |
exog |
Union[pandas.core.series.Series, pandas.core.frame.DataFrame] |
Exogenous variable/s included as predictor/s. |
None |
Returns:
| Type | Description |
|---|---|
Series |
Predicted values. |
Source code in skforecast/ForecasterAutoregDirect/ForecasterAutoregDirect.py
def predict(
self,
steps: Optional[Union[int, list]]=None,
last_window: Optional[pd.Series]=None,
exog: Optional[Union[pd.Series, pd.DataFrame]]=None
) -> pd.Series:
"""
Predict n steps ahead.
Parameters
----------
steps : int, list, None, default `None`
Predict n steps. The value of `steps` must be less than or equal to the
value of steps defined when initializing the forecaster. Starts at 1.
If `int`:
Only steps within the range of 1 to int are predicted.
If `list`:
List of ints. Only the steps contained in the list are predicted.
If `None`:
As many steps are predicted as were defined at initialization.
last_window : pandas Series, default `None`
Series values used to create the predictors (lags) needed in the
first iteration of the prediction (t + 1).
If `last_window = None`, the values stored in` self.last_window` are
used to calculate the initial predictors, and the predictions start
right after training data.
exog : pandas Series, pandas DataFrame, default `None`
Exogenous variable/s included as predictor/s.
Returns
-------
predictions : pandas Series
Predicted values.
"""
if isinstance(steps, int):
steps = list(np.arange(steps) + 1)
elif steps is None:
steps = list(np.arange(self.steps) + 1)
elif isinstance(steps, list):
steps = list(np.array(steps))
for step in steps:
if not isinstance(step, (int, np.int64, np.int32)):
raise TypeError(
(f"`steps` argument must be an int, a list of ints or `None`. "
f"Got {type(steps)}.")
)
if last_window is None:
last_window = copy(self.last_window)
check_predict_input(
forecaster_name = type(self).__name__,
steps = steps,
fitted = self.fitted,
included_exog = self.included_exog,
index_type = self.index_type,
index_freq = self.index_freq,
window_size = self.window_size,
last_window = last_window,
last_window_exog = None,
exog = exog,
exog_type = self.exog_type,
exog_col_names = self.exog_col_names,
interval = None,
alpha = None,
max_steps = self.steps,
levels = None,
series_col_names = None
)
if exog is not None:
if isinstance(exog, pd.DataFrame):
exog = transform_dataframe(
df = exog,
transformer = self.transformer_exog,
fit = False,
inverse_transform = False
)
else:
exog = transform_series(
series = exog,
transformer = self.transformer_exog,
fit = False,
inverse_transform = False
)
check_exog_dtypes(exog=exog)
exog_values = exog_to_direct(exog=exog.iloc[:max(steps), ], steps=max(steps)).to_numpy()
else:
exog_values = None
last_window = transform_series(
series = last_window,
transformer = self.transformer_y,
fit = False,
inverse_transform = False
)
last_window_values, last_window_index = preprocess_last_window(
last_window = last_window
)
X_lags = last_window_values[-self.lags].reshape(1, -1)
if exog is None:
Xs = [X_lags] * len(steps)
else:
Xs = [
np.hstack([X_lags, exog_values[0][step-1::max(steps)].reshape(1, -1)])
for step in steps
]
regressors = [self.regressors_[step] for step in steps]
with warnings.catch_warnings():
# Suppress scikit-learn warning: "X does not have valid feature names,
# but NoOpTransformer was fitted with feature names".
warnings.simplefilter("ignore")
predictions = [
regressor.predict(X)[0] for regressor, X in zip(regressors, Xs)
]
idx = expand_index(index=last_window_index, steps=max(steps))
predictions = pd.Series(
data = predictions,
index = idx[np.array(steps)-1],
name = 'pred'
)
predictions = transform_series(
series = predictions,
transformer = self.transformer_y,
fit = False,
inverse_transform = True
)
return predictions
predict_bootstrapping(self, steps=None, last_window=None, exog=None, n_boot=500, random_state=123, in_sample_residuals=True)
¶
Generate multiple forecasting predictions using a bootstrapping process.
By sampling from a collection of past observed errors (the residuals), each iteration of bootstrapping generates a different set of predictions. See the Notes section for more information.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
steps |
Union[int, list] |
Predict n steps. The value of If If If |
None |
last_window |
Optional[pandas.core.series.Series] |
Series values used to create the predictors (lags) needed in the first iteration of the prediction (t + 1). If |
None |
exog |
Union[pandas.core.series.Series, pandas.core.frame.DataFrame] |
Exogenous variable/s included as predictor/s. |
None |
n_boot |
int |
Number of bootstrapping iterations used to estimate prediction intervals. |
500 |
random_state |
int |
Sets a seed to the random generator, so that boot intervals are always deterministic. |
123 |
in_sample_residuals |
bool |
If |
True |
Returns:
| Type | Description |
|---|---|
DataFrame |
Predictions generated by bootstrapping. |
Source code in skforecast/ForecasterAutoregDirect/ForecasterAutoregDirect.py
def predict_bootstrapping(
self,
steps: Optional[Union[int, list]]=None,
last_window: Optional[pd.Series]=None,
exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
n_boot: int=500,
random_state: int=123,
in_sample_residuals: bool=True
) -> pd.DataFrame:
"""
Generate multiple forecasting predictions using a bootstrapping process.
By sampling from a collection of past observed errors (the residuals),
each iteration of bootstrapping generates a different set of predictions.
See the Notes section for more information.
Parameters
----------
steps : int, list, None, default `None`
Predict n steps. The value of `steps` must be less than or equal to the
value of steps defined when initializing the forecaster. Starts at 1.
If `int`:
Only steps within the range of 1 to int are predicted.
If `list`:
List of ints. Only the steps contained in the list are predicted.
If `None`:
As many steps are predicted as were defined at initialization.
last_window : pandas Series, default `None`
Series values used to create the predictors (lags) needed in the
first iteration of the prediction (t + 1).
If `last_window = None`, the values stored in` self.last_window` are
used to calculate the initial predictors, and the predictions start
right after training data.
exog : pandas Series, pandas DataFrame, default `None`
Exogenous variable/s included as predictor/s.
n_boot : int, default `500`
Number of bootstrapping iterations used to estimate prediction
intervals.
random_state : int, default `123`
Sets a seed to the random generator, so that boot intervals are always
deterministic.
in_sample_residuals : bool, default `True`
If `True`, residuals from the training data are used as proxy of
prediction error to create prediction intervals. If `False`, out of
sample residuals are used. In the latter case, the user should have
calculated and stored the residuals within the forecaster (see
`set_out_sample_residuals()`).
Returns
-------
boot_predictions : pandas DataFrame, shape (steps, n_boot)
Predictions generated by bootstrapping.
Notes
-----
More information about prediction intervals in forecasting:
https://otexts.com/fpp3/prediction-intervals.html#prediction-intervals-from-bootstrapped-residuals
Forecasting: Principles and Practice (3nd ed) Rob J Hyndman and George Athanasopoulos.
"""
if isinstance(steps, int):
steps = list(np.arange(steps) + 1)
elif steps is None:
steps = list(np.arange(self.steps) + 1)
elif isinstance(steps, list):
steps = list(np.array(steps))
if in_sample_residuals:
if not set(steps).issubset(set(self.in_sample_residuals.keys())):
raise ValueError(
(f"Not `forecaster.in_sample_residuals` for steps: "
f"{set(steps) - set(self.in_sample_residuals.keys())}.")
)
residuals = self.in_sample_residuals
else:
if self.out_sample_residuals is None:
raise ValueError(
("`forecaster.out_sample_residuals` is `None`. Use "
"`in_sample_residuals=True` or method `set_out_sample_residuals()` "
"before `predict_interval()`, `predict_bootstrapping()` or "
"`predict_dist()`.")
)
else:
if not set(steps).issubset(set(self.out_sample_residuals.keys())):
raise ValueError(
(f"Not `forecaster.out_sample_residuals` for steps: "
f"{set(steps) - set(self.out_sample_residuals.keys())}. "
f"Use method `set_out_sample_residuals()`.")
)
residuals = self.out_sample_residuals
check_residuals = "forecaster.in_sample_residuals" if in_sample_residuals else "forecaster.out_sample_residuals"
for step in steps:
if residuals[step] is None:
raise ValueError(
(f"forecaster residuals for step {step} are `None`. Check {check_residuals}.")
)
elif (residuals[step] == None).any():
raise ValueError(
(f"forecaster residuals for step {step} contains `None` values. Check {check_residuals}.")
)
predictions = self.predict(
steps = steps,
last_window = last_window,
exog = exog
)
# Predictions must be in the transformed scale before adding residuals
predictions = transform_series(
series = predictions,
transformer = self.transformer_y,
fit = False,
inverse_transform = False
)
boot_predictions = pd.concat([predictions] * n_boot, axis=1)
boot_predictions.columns= [f"pred_boot_{i}" for i in range(n_boot)]
rng = np.random.default_rng(seed=random_state)
for i, step in enumerate(steps):
sample_residuals = rng.choice(
a = residuals[step],
size = n_boot,
replace = True
)
boot_predictions.iloc[i, :] = boot_predictions.iloc[i, :] + sample_residuals
if self.transformer_y:
for col in boot_predictions.columns:
boot_predictions[col] = transform_series(
series = boot_predictions[col],
transformer = self.transformer_y,
fit = False,
inverse_transform = True
)
return boot_predictions
predict_dist(self, distribution, steps=None, last_window=None, exog=None, n_boot=500, random_state=123, in_sample_residuals=True)
¶
Fit a given probability distribution for each step. After generating
multiple forecasting predictions through a bootstrapping process, each step is fitted to the given distribution.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
distribution |
object |
A distribution object from scipy.stats. |
required |
steps |
Union[int, list] |
Predict n steps. The value of If If If |
None |
last_window |
Optional[pandas.core.series.Series] |
Series values used to create the predictors (lags) needed in the first iteration of the prediction (t + 1). If |
None |
exog |
Union[pandas.core.series.Series, pandas.core.frame.DataFrame] |
Exogenous variable/s included as predictor/s. |
None |
n_boot |
int |
Number of bootstrapping iterations used to estimate prediction intervals. |
500 |
random_state |
int |
Sets a seed to the random generator, so that boot intervals are always deterministic. |
123 |
in_sample_residuals |
bool |
If |
True |
Returns:
| Type | Description |
|---|---|
DataFrame |
Distribution parameters estimated for each step. |
Source code in skforecast/ForecasterAutoregDirect/ForecasterAutoregDirect.py
def predict_dist(
self,
distribution: object,
steps: Optional[Union[int, list]]=None,
last_window: Optional[pd.Series]=None,
exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
n_boot: int=500,
random_state: int=123,
in_sample_residuals: bool=True
) -> pd.DataFrame:
"""
Fit a given probability distribution for each step. After generating
multiple forecasting predictions through a bootstrapping process, each
step is fitted to the given distribution.
Parameters
----------
distribution : Object
A distribution object from scipy.stats.
steps : int, list, None, default `None`
Predict n steps. The value of `steps` must be less than or equal to the
value of steps defined when initializing the forecaster. Starts at 1.
If `int`:
Only steps within the range of 1 to int are predicted.
If `list`:
List of ints. Only the steps contained in the list are predicted.
If `None`:
As many steps are predicted as were defined at initialization.
last_window : pandas Series, default `None`
Series values used to create the predictors (lags) needed in the
first iteration of the prediction (t + 1).
If `last_window = None`, the values stored in` self.last_window` are
used to calculate the initial predictors, and the predictions start
right after training data.
exog : pandas Series, pandas DataFrame, default `None`
Exogenous variable/s included as predictor/s.
n_boot : int, default `500`
Number of bootstrapping iterations used to estimate prediction
intervals.
random_state : int, default `123`
Sets a seed to the random generator, so that boot intervals are always
deterministic.
in_sample_residuals : bool, default `True`
If `True`, residuals from the training data are used as proxy of
prediction error to create prediction intervals. If `False`, out of
sample residuals are used. In the latter case, the user should have
calculated and stored the residuals within the forecaster (see
`set_out_sample_residuals()`).
Returns
-------
predictions : pandas DataFrame
Distribution parameters estimated for each step.
"""
boot_samples = self.predict_bootstrapping(
steps = steps,
last_window = last_window,
exog = exog,
n_boot = n_boot,
random_state = random_state,
in_sample_residuals = in_sample_residuals
)
param_names = [p for p in inspect.signature(distribution._pdf).parameters if not p=='x'] + ["loc","scale"]
param_values = np.apply_along_axis(lambda x: distribution.fit(x), axis=1, arr=boot_samples)
predictions = pd.DataFrame(
data = param_values,
columns = param_names,
index = boot_samples.index
)
return predictions
predict_interval(self, steps=None, last_window=None, exog=None, interval=[5, 95], n_boot=500, random_state=123, in_sample_residuals=True)
¶
Bootstrapping based prediction intervals.
Both predictions and intervals are returned.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
steps |
Union[int, list] |
Predict n steps. The value of If If If |
None |
last_window |
Optional[pandas.core.series.Series] |
Series values used to create the predictors (lags) needed in the first iteration of the prediction (t + 1). If |
None |
exog |
Union[pandas.core.series.Series, pandas.core.frame.DataFrame] |
Exogenous variable/s included as predictor/s. |
None |
interval |
list |
Confidence of the prediction interval estimated. Sequence of
percentiles to compute, which must be between 0 and 100 inclusive.
For example, interval of 95% should be as |
[5, 95] |
n_boot |
int |
Number of bootstrapping iterations used to estimate prediction intervals. |
500 |
random_state |
int |
Sets a seed to the random generator, so that boot intervals are always deterministic. |
123 |
in_sample_residuals |
bool |
If |
True |
Returns:
| Type | Description |
|---|---|
DataFrame |
Values predicted by the forecaster and their estimated interval:
|
Source code in skforecast/ForecasterAutoregDirect/ForecasterAutoregDirect.py
def predict_interval(
self,
steps: Optional[Union[int, list]]=None,
last_window: Optional[pd.Series]=None,
exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
interval: list=[5, 95],
n_boot: int=500,
random_state: int=123,
in_sample_residuals: bool=True
) -> pd.DataFrame:
"""
Bootstrapping based prediction intervals.
Both predictions and intervals are returned.
Parameters
----------
steps : int, list, None, default `None`
Predict n steps. The value of `steps` must be less than or equal to the
value of steps defined when initializing the forecaster. Starts at 1.
If `int`:
Only steps within the range of 1 to int are predicted.
If `list`:
List of ints. Only the steps contained in the list are predicted.
If `None`:
As many steps are predicted as were defined at initialization.
last_window : pandas Series, default `None`
Series values used to create the predictors (lags) needed in the
first iteration of the prediction (t + 1).
If `last_window = None`, the values stored in` self.last_window` are
used to calculate the initial predictors, and the predictions start
right after training data.
exog : pandas Series, pandas DataFrame, default `None`
Exogenous variable/s included as predictor/s.
interval : list, default `[5, 95]`
Confidence of the prediction interval estimated. Sequence of
percentiles to compute, which must be between 0 and 100 inclusive.
For example, interval of 95% should be as `interval = [2.5, 97.5]`.
n_boot : int, default `500`
Number of bootstrapping iterations used to estimate prediction
intervals.
random_state : int, default `123`
Sets a seed to the random generator, so that boot intervals are always
deterministic.
in_sample_residuals : bool, default `True`
If `True`, residuals from the training data are used as proxy of
prediction error to create prediction intervals. If `False`, out of
sample residuals are used. In the latter case, the user should have
calculated and stored the residuals within the forecaster (see
`set_out_sample_residuals()`).
Returns
-------
predictions : pandas DataFrame
Values predicted by the forecaster and their estimated interval:
- pred: predictions.
- lower_bound: lower bound of the interval.
- upper_bound: upper bound interval of the interval.
Notes
-----
More information about prediction intervals in forecasting:
https://otexts.com/fpp2/prediction-intervals.html
Forecasting: Principles and Practice (2nd ed) Rob J Hyndman and
George Athanasopoulos.
"""
check_interval(interval=interval)
predictions = self.predict(
steps = steps,
last_window = last_window,
exog = exog
)
boot_predictions = self.predict_bootstrapping(
steps = steps,
last_window = last_window,
exog = exog,
n_boot = n_boot,
random_state = random_state,
in_sample_residuals = in_sample_residuals
)
interval = np.array(interval)/100
predictions_interval = boot_predictions.quantile(q=interval, axis=1).transpose()
predictions_interval.columns = ['lower_bound', 'upper_bound']
predictions = pd.concat((predictions, predictions_interval), axis=1)
return predictions
predict_pandas(self, steps=None, last_window=None, exog=None)
¶
Equivalent to predict() but using pandas instead of numpy.
Source code in skforecast/ForecasterAutoregDirect/ForecasterAutoregDirect.py
def predict_pandas(
self,
steps: Optional[Union[int, list]]=None,
last_window: Optional[pd.Series]=None,
exog: Optional[Union[pd.Series, pd.DataFrame]]=None
) -> pd.Series: # pragma: no cover
"""
Equivalent to predict() but using pandas instead of numpy.
"""
if isinstance(steps, int):
steps = list(np.arange(steps) + 1)
elif steps is None:
steps = list(np.arange(self.steps) + 1)
elif isinstance(steps, list):
steps = list(np.array(steps))
for step in steps:
if not isinstance(step, (int, np.int64, np.int32)):
raise TypeError(
(f"`steps` argument must be an int, a list of ints or `None`. "
f"Got {type(steps)}.")
)
if last_window is None:
last_window = copy(self.last_window)
_, last_window_index = preprocess_last_window(
last_window = last_window,
return_values = False
)
idx = expand_index(index=last_window_index, steps=max(steps))
X_lags = last_window.iloc[-self.lags]
X_lags.index = [f"lag_{lag}" for lag in self.lags]
X_lags = X_lags.to_frame().T
if exog is None:
Xs = [X_lags] * len(steps)
else:
Xs = [
pd.concat([X_lags, exog.iloc[step-1::max(steps)]], axis=1)
for step in steps
]
regressors = [self.regressors_[step] for step in steps]
predictions = [regressor.predict(X)[0] for regressor, X in zip(regressors, Xs)]
predictions = pd.Series(
data = predictions,
index = idx[np.array(steps)-1],
name = 'pred',
)
return predictions
set_fit_kwargs(self, fit_kwargs)
¶
Set new values for the additional keyword arguments passed to the fit
method of the regressor.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
fit_kwargs |
dict |
Dict of the form {"argument": new_value}. |
required |
Source code in skforecast/ForecasterAutoregDirect/ForecasterAutoregDirect.py
def set_fit_kwargs(
self,
fit_kwargs: dict
) -> None:
"""
Set new values for the additional keyword arguments passed to the `fit`
method of the regressor.
Parameters
----------
fit_kwargs : dict
Dict of the form {"argument": new_value}.
Returns
-------
None
"""
self.fit_kwargs = check_select_fit_kwargs(self.regressor, fit_kwargs=fit_kwargs)
set_lags(self, lags)
¶
Set new value to the attribute lags.
Attributes max_lag and window_size are also updated.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
lags |
Union[int, list, numpy.ndarray, range] |
Lags used as predictors. Index starts at 1, so lag 1 is equal to t-1.
|
required |
Source code in skforecast/ForecasterAutoregDirect/ForecasterAutoregDirect.py
def set_lags(
self,
lags: Union[int, list, np.ndarray, range]
) -> None:
"""
Set new value to the attribute `lags`.
Attributes `max_lag` and `window_size` are also updated.
Parameters
----------
lags : int, list, 1D np.ndarray, range
Lags used as predictors. Index starts at 1, so lag 1 is equal to t-1.
`int`: include lags from 1 to `lags`.
`list` or `np.ndarray`: include only lags present in `lags`.
Returns
-------
None
"""
self.lags = initialize_lags(type(self).__name__, lags)
self.max_lag = max(self.lags)
self.window_size = max(self.lags)
set_out_sample_residuals(self, residuals, append=True, transform=True, random_state=123)
¶
Set new values to the attribute out_sample_residuals. Out of sample
residuals are meant to be calculated using observations that did not participate in the training process.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
residuals |
dict |
Dictionary of numpy ndarrays with the residuals of each model in the form {step: residuals}. If len(residuals) > 1000, only a random sample of 1000 values are stored. |
required |
append |
bool |
If |
True |
transform |
bool |
If |
True |
random_state |
int |
Sets a seed to the random sampling for reproducible output. |
123 |
Source code in skforecast/ForecasterAutoregDirect/ForecasterAutoregDirect.py
def set_out_sample_residuals(
self,
residuals: dict,
append: bool=True,
transform: bool=True,
random_state: int=123
)-> None:
"""
Set new values to the attribute `out_sample_residuals`. Out of sample
residuals are meant to be calculated using observations that did not
participate in the training process.
Parameters
----------
residuals : dict
Dictionary of numpy ndarrays with the residuals of each model in the
form {step: residuals}. If len(residuals) > 1000, only a random
sample of 1000 values are stored.
append : bool, default `True`
If `True`, new residuals are added to the once already stored in the
attribute `out_sample_residuals`. Once the limit of 1000 values is
reached, no more values are appended. If False, `out_sample_residuals`
is overwritten with the new residuals.
transform : bool, default `True`
If `True`, new residuals are transformed using self.transformer_y.
random_state : int, default `123`
Sets a seed to the random sampling for reproducible output.
Returns
-------
self
"""
if not isinstance(residuals, dict) or not all(isinstance(x, np.ndarray) for x in residuals.values()):
raise TypeError(
f"`residuals` argument must be a dict of numpy ndarrays in the form "
"`{step: residuals}`. "
f"Got {type(residuals)}."
)
if not self.fitted:
raise sklearn.exceptions.NotFittedError(
("This forecaster is not fitted yet. Call `fit` with appropriate "
"arguments before using `set_out_sample_residuals()`.")
)
if self.out_sample_residuals is None:
self.out_sample_residuals = {step: None for step in range(1, self.steps + 1)}
if not set(self.out_sample_residuals.keys()).issubset(set(residuals.keys())):
warnings.warn(
f"""
Only residuals of models (steps)
{set(self.out_sample_residuals.keys()).intersection(set(residuals.keys()))}
are updated.
"""
)
residuals = {key: value for key, value in residuals.items() if key in self.out_sample_residuals.keys()}
if not transform and self.transformer_y is not None:
warnings.warn(
(f"Argument `transform` is set to `False` but forecaster was trained "
f"using a transformer {self.transformer_y}. Ensure that the new residuals "
f"are already transformed or set `transform=True`.")
)
if transform and self.transformer_y is not None:
warnings.warn(
(f"Residuals will be transformed using the same transformer used "
f"when training the forecaster ({self.transformer_y}). Ensure the "
f"new residuals are on the same scale as the original time series.")
)
for key, value in residuals.items():
residuals[key] = transform_series(
series = pd.Series(value, name='residuals'),
transformer = self.transformer_y,
fit = False,
inverse_transform = False
).to_numpy()
for key, value in residuals.items():
if len(value) > 1000:
rng = np.random.default_rng(seed=random_state)
value = rng.choice(a=value, size=1000, replace=False)
if append and self.out_sample_residuals[key] is not None:
free_space = max(0, 1000 - len(self.out_sample_residuals[key]))
if len(value) < free_space:
value = np.hstack((
self.out_sample_residuals[key],
value
))
else:
value = np.hstack((
self.out_sample_residuals[key],
value[:free_space]
))
self.out_sample_residuals[key] = value
set_params(self, params)
¶
Set new values to the parameters of the scikit learn model stored in the
forecaster. It is important to note that all models share the same configuration of parameters and hyperparameters.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
params |
dict |
Parameters values. |
required |
Source code in skforecast/ForecasterAutoregDirect/ForecasterAutoregDirect.py
def set_params(
self,
params: dict
) -> None:
"""
Set new values to the parameters of the scikit learn model stored in the
forecaster. It is important to note that all models share the same
configuration of parameters and hyperparameters.
Parameters
----------
params : dict
Parameters values.
Returns
-------
self
"""
self.regressor = clone(self.regressor)
self.regressor.set_params(**params)
self.regressors_ = {step: clone(self.regressor) for step in range(1, self.steps + 1)}
_create_lags(self, y)
private
¶
Transforms a 1d array into a 2d array (X) and a 1d array (y). Each row
in X is associated with a value of y and it represents the lags that precede it.
Notice that, the returned matrix X_data, contains the lag 1 in the first column, the lag 2 in the second column and so on.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
y |
ndarray |
Training time series. |
required |
Returns:
| Type | Description |
|---|---|
Tuple[numpy.ndarray, numpy.ndarray] |
2d numpy array with the lagged values (predictors). |
Source code in skforecast/ForecasterAutoreg/ForecasterAutoreg.py
def _create_lags(
self,
y: np.ndarray
) -> Tuple[np.ndarray, np.ndarray]:
"""
Transforms a 1d array into a 2d array (X) and a 1d array (y). Each row
in X is associated with a value of y and it represents the lags that
precede it.
Notice that, the returned matrix X_data, contains the lag 1 in the first
column, the lag 2 in the second column and so on.
Parameters
----------
y : 1d numpy ndarray
Training time series.
Returns
-------
X_data : 2d numpy ndarray, shape (samples - max(self.lags), len(self.lags))
2d numpy array with the lagged values (predictors).
y_data : 1d numpy ndarray, shape (samples - max(self.lags),)
Values of the time series related to each row of `X_data`.
"""
n_splits = len(y) - self.max_lag
if n_splits <= 0:
raise ValueError(
f"The maximum lag ({self.max_lag}) must be less than the length "
f"of the series ({len(y)})."
)
X_data = np.full(shape=(n_splits, len(self.lags)), fill_value=np.nan, dtype=float)
for i, lag in enumerate(self.lags):
X_data[:, i] = y[self.max_lag - lag: -lag]
y_data = y[self.max_lag:]
return X_data, y_data
__repr__(self)
special
¶
Information displayed when a ForecasterAutoreg object is printed.
Source code in skforecast/ForecasterAutoreg/ForecasterAutoreg.py
def __repr__(
self
) -> str:
"""
Information displayed when a ForecasterAutoreg object is printed.
"""
if isinstance(self.regressor, sklearn.pipeline.Pipeline):
name_pipe_steps = tuple(name + "__" for name in self.regressor.named_steps.keys())
params = {key : value for key, value in self.regressor.get_params().items() \
if key.startswith(name_pipe_steps)}
else:
params = self.regressor.get_params(deep=True)
info = (
f"{'=' * len(type(self).__name__)} \n"
f"{type(self).__name__} \n"
f"{'=' * len(type(self).__name__)} \n"
f"Regressor: {self.regressor} \n"
f"Lags: {self.lags} \n"
f"Transformer for y: {self.transformer_y} \n"
f"Transformer for exog: {self.transformer_exog} \n"
f"Window size: {self.window_size} \n"
f"Weight function included: {True if self.weight_func is not None else False} \n"
f"Exogenous included: {self.included_exog} \n"
f"Type of exogenous variable: {self.exog_type} \n"
f"Exogenous variables names: {self.exog_col_names} \n"
f"Training range: {self.training_range.to_list() if self.fitted else None} \n"
f"Training index type: {str(self.index_type).split('.')[-1][:-2] if self.fitted else None} \n"
f"Training index frequency: {self.index_freq if self.fitted else None} \n"
f"Regressor parameters: {params} \n"
f"fit_kwargs: {self.fit_kwargs} \n"
f"Creation date: {self.creation_date} \n"
f"Last fit date: {self.fit_date} \n"
f"Skforecast version: {self.skforcast_version} \n"
f"Python version: {self.python_version} \n"
f"Forecaster id: {self.forecaster_id} \n"
)
return info