SkforecastRecursive

Adapter for skforecast.recursive.ForecasterRecursive class [1].

This class turns any regressor compatible with the scikit-learn API into a recursive autoregressive (multi-step) forecaster.

Parameters:

regressorregressor or pipeline compatible with the scikit-learn API

An instance of a regressor or pipeline compatible with the scikit-learn API

lagsint, list, numpy ndarray, range, default None

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, 1d numpy ndarray or range: include only lags present in

lags, all elements must be int. - None: no lags are included as predictors.

window_featuresobject, list, default None

Instance or list of instances used to create window features. Window features are created from the original time series and are included as predictors. This argument is meant to work with RollingFeatures class [2].

transformer_yobject 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. ColumnTransformer’s are not allowed since they do not have inverse_transform method. The transformation is applied to y before training the forecaster.

transformer_Xobject transformer (preprocessor), default None

An instance of a transformer (preprocessor) compatible with the scikit-learn preprocessing API. The transformation is applied to X before training the forecaster. inverse_transform is not available when using ColumnTransformer’s.

weight_funcCallable, 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.

differentiationint, default None

Order of differencing applied to the time series before training the forecaster. If None, no differencing is applied. The order of differentiation is the number of times the differencing operation is applied to a time series. Differencing involves computing the differences between consecutive data points in the series. Differentiation is reversed in the output of predict() and predict_interval().

fit_kwargsdict, default None

Additional arguments to be passed to the fit method of the regressor.

binner_kwargsdict, default None

Additional arguments to pass to the QuantileBinner class [3] used to discretize the residuals into k bins according to the predicted values associated with each residual. Available arguments are:

n_bins
method
subsample
random_state
dtype

store_in_sample_residualsbool, default False

If True, stores the in-sample residuals when fitting the forecaster. This is required if you want to use predict_quantiles later. If False, predict_quantiles will raise an error unless you call set_in_sample_residuals() manually.

Argument method is passed internally to the function numpy.percentile.

Attributes:

cutoff: Cut-off = “present time” state of forecaster.
fh: Forecasting horizon that was passed.
is_fitted: Whether fit has been called.
state: State of the estimator.

References

[1]

https://skforecast.org/latest/api/forecasterrecursive#forecasterrecursive

[2]

https://skforecast.org/0.14.0/api/preprocessing#skforecast.preprocessing.preprocessing.RollingFeatures

[3]

https://skforecast.org/latest/api/preprocessing.html#skforecast.preprocessing.preprocessing.QuantileBinner

Examples

>>> from sktime.forecasting.compose import SkforecastRecursive

Without exogenous features

>>> from sklearn.linear_model import LinearRegression
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> forecaster = SkforecastRecursive(
...     LinearRegression(), 2
... )
>>> forecaster.fit(y)
SkforecastRecursive(lags=2, regressor=LinearRegression())
>>> y_pred = forecaster.predict(fh=[1, 2, 3])
>>> y_pred_int = forecaster.predict_interval(
...     fh=[2], coverage=[0.9, 0.95]
... )
>>> y_pred_qtl = forecaster.predict_quantiles(
...     fh=[1, 3], alpha=[0.8, 0.3, 0.2, 0.7]
... )

With exogenous features

>>> from sklearn.ensemble import RandomForestRegressor
>>> from sktime.datasets import load_longley
>>> y, X = load_longley()
>>> y_train = y.head(n=12)
>>> y_test = y.tail(n=4)
>>> X_train = X.head(n=12)
>>> X_test = X.tail(n=4)
>>> forecaster = SkforecastRecursive(
...     RandomForestRegressor(), [2, 4]
... )
>>> forecaster.fit(y_train, X=X_train)
SkforecastRecursive(lags=[2, 4], regressor=RandomForestRegressor())
>>> y_pred = forecaster.predict(fh=[1, 2, 3], X=X_test)
>>> y_pred_int = forecaster.predict_interval(
...     fh=[1, 3], X=X_test, coverage=[0.6, 0.4]
... )
>>> y_pred_qtl = forecaster.predict_quantiles(
...     fh=[1, 3], X=X_test, alpha=[0.01, 0.5]
... )

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(y[, X, fh])	Fit forecaster to training data.
`fit_predict`(y[, X, fh, X_pred])	Fit and forecast time series at future horizon.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_pretrained_params`([deep])	Get pretrained parameters of this estimator.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`([fh, X])	Forecast time series at future horizon.
`predict_interval`([fh, X, coverage])	Compute/return prediction interval forecasts.
`predict_proba`([fh, X, marginal])	Compute/return fully probabilistic forecasts.
`predict_quantiles`([fh, X, alpha])	Compute/return quantile forecasts.
`predict_residuals`([y, X])	Return residuals of time series forecasts.
`predict_var`([fh, X, cov])	Compute/return variance forecasts.
`pretrain`(y[, X, fh])	Pre-train forecaster on panel (global) data.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`score`(y[, X, fh])	Scores forecast against ground truth, using MAPE (non-symmetric).
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`update`(y[, X, update_params])	Update cutoff value and, optionally, fitted parameters.
`update_predict`(y[, cv, X, update_params, ...])	Make predictions and update model iteratively over the test set.
`update_predict_single`([y, fh, X, update_params])	Update model with new data and make forecasts.