ForecasterAutoregMultiVariate
¶
ForecasterAutoregMultiVariate(regressor, level, steps, lags, transformer_series=StandardScaler(), transformer_exog=None, weight_func=None, fit_kwargs=None, n_jobs='auto', forecaster_id=None)
¶
Bases: ForecasterBase
This class turns any regressor compatible with the scikit-learn API into a autoregressive multivariate 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 |
regressor or pipeline compatible with the scikit-learn API
|
An instance of a regressor or pipeline compatible with the scikit-learn API. |
required |
level |
str
|
Name of the time series to be predicted. |
required |
steps |
int
|
Maximum number of future steps the forecaster will predict when using
method |
required |
lags |
int, list, numpy ndarray, range, dict
|
Lags used as predictors. Index starts at 1, so lag 1 is equal to t-1.
|
required |
transformer_series |
(transformer(preprocessor), dict)
|
An instance of a transformer (preprocessor) compatible with the scikit-learn
preprocessing API with methods: fit, transform, fit_transform and
inverse_transform. Transformation is applied to each
|
`sklearn.preprocessing.StandardScaler`
|
transformer_exog |
transformer
|
An instance of a transformer (preprocessor) compatible with the scikit-learn
preprocessing API. The transformation is applied to |
`None`
|
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 |
`None`
|
fit_kwargs |
dict
|
Additional arguments to be passed to the |
`None`
|
n_jobs |
(int, auto)
|
The number of jobs to run in parallel. If |
`'auto'`
|
forecaster_id |
(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, dict
|
Lags used as predictors. |
lags_ |
dict
|
Dictionary containing the lags of each series. Created from |
transformer_series |
transformer (preprocessor), dict, default `None`
|
An instance of a transformer (preprocessor) compatible with the scikit-learn
preprocessing API with methods: fit, transform, fit_transform and
inverse_transform. Transformation is applied to each
|
transformer_series_ |
dict
|
Dictionary with the transformer for each series. It is created cloning the
objects in |
transformer_exog |
transformer
|
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. |
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
|
window_size_diff |
int
|
This attribute has the same value as window_size as this Forecaster doesn't support differentiation. Present here for API consistency. |
last_window |
pandas Series
|
Last window seen by the forecaster 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 the exogenous variables used during training. |
series_col_names |
list
|
Names of the series used during training. |
series_X_train |
list
|
Names of the series added to |
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. |
skforecast_version |
str
|
Version of skforecast library used to create the forecaster. |
python_version |
str
|
Version of python used to create the forecaster. |
n_jobs |
int, 'auto', default `'auto'`
|
The number of jobs to run in parallel. If |
forecaster_id |
(str, int)
|
Name used as an identifier of the forecaster. |
dropna_from_series |
Ignored
|
Not used, present here for API consistency by convention. |
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.
Source code in skforecast\ForecasterAutoregMultiVariate\ForecasterAutoregMultiVariate.py
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|
_create_lags(y, lags, return_data='both')
¶
Transforms a 1d array into a 2d array (X) and a 2d 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 |
numpy ndarray
|
1d numpy ndarray Training time series. |
required |
lags |
numpy ndarray
|
lags to create. |
required |
return_data |
str
|
Specifies which data to return. Options are 'X', 'y', 'both'. |
'both'
|
Returns:
Name | Type | Description |
---|---|---|
X_data |
numpy ndarray
|
2d numpy ndarray with the lagged values (predictors). Will be None if
|
y_data |
numpy ndarray
|
2d numpy ndarray with the values of the time series related to each
row of |
Source code in skforecast\ForecasterAutoregMultiVariate\ForecasterAutoregMultiVariate.py
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|
_create_train_X_y(series, exog=None)
¶
Create training matrices from multiple 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 |
---|---|---|---|
series |
pandas DataFrame
|
Training time series. |
required |
exog |
pandas Series, pandas DataFrame
|
Exogenous variable/s included as predictor/s. Must have the same
number of observations as |
`None`
|
Returns:
Name | Type | Description |
---|---|---|
X_train |
pandas DataFrame
|
Training values (predictors) for each step. Note that the index corresponds to that of the last step. It is updated for the corresponding step in the filter_train_X_y_for_step method. Shape: (len(series) - self.max_lag, len(self.lags)len(series.columns) + exog.shape[1]steps) |
y_train |
dict
|
Values (target) of the time series related to each row of |
series_col_names |
list
|
Names of the series (levels) provided by the user during training. |
series_X_train |
list
|
Names of the series added to |
exog_col_names |
list
|
Names of the exogenous variables included in the training matrices. |
Source code in skforecast\ForecasterAutoregMultiVariate\ForecasterAutoregMultiVariate.py
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|
create_train_X_y(series, exog=None)
¶
Create training matrices from multiple 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 |
---|---|---|---|
series |
pandas DataFrame
|
Training time series. |
required |
exog |
pandas Series, pandas DataFrame
|
Exogenous variable/s included as predictor/s. Must have the same
number of observations as |
`None`
|
Returns:
Name | Type | Description |
---|---|---|
X_train |
pandas DataFrame
|
Training values (predictors) for each step. Note that the index corresponds to that of the last step. It is updated for the corresponding step in the filter_train_X_y_for_step method. Shape: (len(series) - self.max_lag, len(self.lags)len(series.columns) + exog.shape[1]steps) |
y_train |
dict
|
Values (target) of the time series related to each row of |
Source code in skforecast\ForecasterAutoregMultiVariate\ForecasterAutoregMultiVariate.py
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|
filter_train_X_y_for_step(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
method.
This method updates the index of X_train
to the corresponding one
according to y_train
. 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 |
pandas DataFrame
|
Dataframe created with the |
required |
y_train |
dict
|
Dict created with the |
required |
remove_suffix |
bool
|
If True, suffix "_step_i" is removed from the column names. |
`False`
|
Returns:
Name | Type | Description |
---|---|---|
X_train_step |
pandas DataFrame
|
Training values (predictors) for the selected step. |
y_train_step |
pandas Series
|
Values (target) of the time series related to each row of |
Source code in skforecast\ForecasterAutoregMultiVariate\ForecasterAutoregMultiVariate.py
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|
create_sample_weights(X_train)
¶
Crate weights for each observation according to the forecaster's attribute
weight_func
.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
X_train |
pandas DataFrame
|
Dataframe created with |
required |
Returns:
Name | Type | Description |
---|---|---|
sample_weight |
numpy ndarray
|
Weights to use in |
Source code in skforecast\ForecasterAutoregMultiVariate\ForecasterAutoregMultiVariate.py
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|
fit(series, exog=None, store_last_window=True, store_in_sample_residuals=True, suppress_warnings=False)
¶
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 |
---|---|---|---|
series |
pandas DataFrame
|
Training time series. |
required |
exog |
pandas Series, pandas DataFrame
|
Exogenous variable/s included as predictor/s. Must have the same
number of observations as |
`None`
|
store_last_window |
bool
|
Whether or not to store the last window of training data. |
`True`
|
store_in_sample_residuals |
bool
|
If |
`True`
|
suppress_warnings |
bool
|
If |
`False`
|
Returns:
Type | Description |
---|---|
None
|
|
Source code in skforecast\ForecasterAutoregMultiVariate\ForecasterAutoregMultiVariate.py
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|
predict(steps=None, last_window=None, exog=None, suppress_warnings=False, levels=None)
¶
Predict n steps ahead
Parameters:
Name | Type | Description | Default |
---|---|---|---|
steps |
(int, list, None)
|
Predict n steps. The value of
|
`None`
|
last_window |
pandas DataFrame
|
Series values used to create the predictors (lags) needed in the
first iteration of the prediction (t + 1).
If |
`None`
|
exog |
pandas Series, pandas DataFrame
|
Exogenous variable/s included as predictor/s. |
`None`
|
suppress_warnings |
bool
|
If |
`False`
|
levels |
Ignored
|
Not used, present here for API consistency by convention. |
None
|
Returns:
Name | Type | Description |
---|---|---|
predictions |
pandas DataFrame
|
Predicted values. |
Source code in skforecast\ForecasterAutoregMultiVariate\ForecasterAutoregMultiVariate.py
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|
predict_bootstrapping(steps=None, last_window=None, exog=None, n_boot=500, random_state=123, in_sample_residuals=True, suppress_warnings=False, levels=None)
¶
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 |
(int, list, None)
|
Predict n steps. The value of
|
`None`
|
last_window |
pandas DataFrame
|
Series values used to create the predictors (lags) needed in the
first iteration of the prediction (t + 1).
If |
`None`
|
exog |
pandas Series, pandas DataFrame
|
Exogenous variable/s included as predictor/s. |
`None`
|
n_boot |
int
|
Number of bootstrapping iterations used to estimate predictions. |
`500`
|
random_state |
int
|
Sets a seed to the random generator, so that boot predictions are always deterministic. |
`123`
|
in_sample_residuals |
bool
|
If |
`True`
|
suppress_warnings |
bool
|
If |
`False`
|
levels |
Ignored
|
Not used, present here for API consistency by convention. |
None
|
Returns:
Name | Type | Description |
---|---|---|
boot_predictions |
pandas DataFrame
|
Predictions generated by bootstrapping. Shape: (steps, n_boot) |
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.
Source code in skforecast\ForecasterAutoregMultiVariate\ForecasterAutoregMultiVariate.py
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|
predict_interval(steps=None, last_window=None, exog=None, interval=[5, 95], n_boot=500, random_state=123, in_sample_residuals=True, suppress_warnings=False, levels=None)
¶
Bootstrapping based predicted intervals. Both predictions and intervals are returned.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
steps |
(int, list, None)
|
Predict n steps. The value of
|
`None`
|
last_window |
pandas DataFrame
|
Series values used to create the predictors (lags) needed in the
first iteration of the prediction (t + 1).
If |
`None`
|
exog |
pandas Series, pandas 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 predictions. |
`500`
|
random_state |
int
|
Sets a seed to the random generator, so that boot predictions are always deterministic. |
`123`
|
in_sample_residuals |
bool
|
If |
`True`
|
suppress_warnings |
bool
|
If |
`False`
|
levels |
Ignored
|
Not used, present here for API consistency by convention. |
None
|
Returns:
Name | Type | Description |
---|---|---|
predictions |
pandas DataFrame
|
Values predicted by the forecaster and their estimated 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.
Source code in skforecast\ForecasterAutoregMultiVariate\ForecasterAutoregMultiVariate.py
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|
predict_quantiles(steps=None, last_window=None, exog=None, quantiles=[0.05, 0.5, 0.95], n_boot=500, random_state=123, in_sample_residuals=True, suppress_warnings=False, levels=None)
¶
Bootstrapping based predicted quantiles.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
steps |
(int, list, None)
|
Predict n steps. The value of
|
`None`
|
last_window |
pandas DataFrame
|
Series values used to create the predictors (lags) needed in the
first iteration of the prediction (t + 1).
If |
`None`
|
exog |
pandas Series, pandas DataFrame
|
Exogenous variable/s included as predictor/s. |
`None`
|
quantiles |
list
|
Sequence of quantiles to compute, which must be between 0 and 1
inclusive. For example, quantiles of 0.05, 0.5 and 0.95 should be as
|
`[0.05, 0.5, 0.95]`
|
n_boot |
int
|
Number of bootstrapping iterations used to estimate quantiles. |
`500`
|
random_state |
int
|
Sets a seed to the random generator, so that boot quantiles are always deterministic. |
`123`
|
in_sample_residuals |
bool
|
If |
`True`
|
suppress_warnings |
bool
|
If |
`False`
|
levels |
Ignored
|
Not used, present here for API consistency by convention. |
None
|
Returns:
Name | Type | Description |
---|---|---|
predictions |
pandas DataFrame
|
Quantiles predicted by the forecaster. |
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.
Source code in skforecast\ForecasterAutoregMultiVariate\ForecasterAutoregMultiVariate.py
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|
predict_dist(distribution, steps=None, last_window=None, exog=None, n_boot=500, random_state=123, in_sample_residuals=True, suppress_warnings=False, levels=None)
¶
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 |
(int, list, None)
|
Predict n steps. The value of
|
`None`
|
last_window |
pandas DataFrame
|
Series values used to create the predictors (lags) needed in the
first iteration of the prediction (t + 1).
If |
`None`
|
exog |
pandas Series, pandas DataFrame
|
Exogenous variable/s included as predictor/s. |
`None`
|
n_boot |
int
|
Number of bootstrapping iterations used to estimate predictions. |
`500`
|
random_state |
int
|
Sets a seed to the random generator, so that boot predictions are always deterministic. |
`123`
|
in_sample_residuals |
bool
|
If |
`True`
|
suppress_warnings |
bool
|
If |
`False`
|
levels |
Ignored
|
Not used, present here for API consistency by convention. |
None
|
Returns:
Name | Type | Description |
---|---|---|
predictions |
pandas DataFrame
|
Distribution parameters estimated for each step. |
Source code in skforecast\ForecasterAutoregMultiVariate\ForecasterAutoregMultiVariate.py
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|
set_params(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 |
Returns:
Type | Description |
---|---|
None
|
|
Source code in skforecast\ForecasterAutoregMultiVariate\ForecasterAutoregMultiVariate.py
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|
set_fit_kwargs(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 |
Returns:
Type | Description |
---|---|
None
|
|
Source code in skforecast\ForecasterAutoregMultiVariate\ForecasterAutoregMultiVariate.py
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|
set_lags(lags)
¶
Set new value to the attribute lags
. Attributes max_lag
,
window_size
and window_size_diff
are also updated.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
lags |
int, list, numpy ndarray, range, dict
|
Lags used as predictors. Index starts at 1, so lag 1 is equal to t-1.
|
required |
Returns:
Type | Description |
---|---|
None
|
|
Source code in skforecast\ForecasterAutoregMultiVariate\ForecasterAutoregMultiVariate.py
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|
set_out_sample_residuals(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`
|
Returns:
Type | Description |
---|---|
None
|
|
Source code in skforecast\ForecasterAutoregMultiVariate\ForecasterAutoregMultiVariate.py
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|
get_feature_importances(step, sort_importance=True)
¶
Return 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_
. Otherwise, it returns
None
.
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 |
sort_importance |
bool
|
If |
True
|
Returns:
Name | Type | Description |
---|---|---|
feature_importances |
pandas DataFrame
|
Feature importances associated with each predictor. |
Source code in skforecast\ForecasterAutoregMultiVariate\ForecasterAutoregMultiVariate.py
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|