Forecaster

SkforecastAutoreg

Adapter for skforecast.ForecasterAutoreg.ForecasterAutoreg class [1].

Quickstart

python

from sktime.forecasting.compose import SkforecastAutoreg

estimator = SkforecastAutoreg(regressor: object, lags: int | ndarray | list, transformer_y: object | None=None, transformer_exog: object | None=None, weight_func: Callable | None=None, differentiation: int | None=None, fit_kwargs: dict | None=None, binner_kwargs: dict | None=None)

Parameters(8)

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: 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.
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. ColumnTransformers are not allowed since they do not have inverse_transform method. The transformation is applied to y before training the forecaster.
transformer_exogobject 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_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 KBinsDiscretizer used to discretize the residuals into k bins according to the predicted values associated with each residual. The `encode’ argument is always set to ‘ordinal’ and `dtype’ to np.float64.

Examples

>>> from sktime.forecasting.compose import SkforecastAutoreg Without exogenous features
>>> from sklearn.linear_model import LinearRegression
>>> from sktime.datasets import load_airline
>>> y = load_airline ()
>>> forecaster = SkforecastAutoreg (
... LinearRegression (), 2
... )
>>> forecaster. fit (y) SkforecastAutoreg(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 = SkforecastAutoreg (
... RandomForestRegressor (), [2, 4 ]
... )
>>> forecaster. fit (y_train, X = X_train) SkforecastAutoreg(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 ]
... )

References

[1 ] https://skforecast.org/latest/api/forecasterautoreg#forecasterautoreg