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ForecasterAutoreg

class
skforecast.ForecasterAutoreg.ForecasterAutoreg(regressor, lags)

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

Parameters
  • regressor (regressor compatible with the scikit-learn API) — An instance of a regressor compatible with the scikit-learn API.
  • lags (int, list, 1D np.array, 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 or np.array: include only lags present in lags.
Attributes
  • exog_shape (tuple) — Shape of exog used in training.
  • exog_type (type) — Type used for the exogenous variable/s: pd.Series, pd.DataFrame or np.ndarray.
  • in_sample_residuals (np.ndarray) — Residuals of the model when predicting training data. Only stored up to 1000 values.
  • included_exog (bool) — If the forecaster has been trained using exogenous variable/s.
  • lags (1D np.array) — Lags used as predictors.
  • last_window (1D np.ndarray) — Last time window the forecaster has seen when trained. It stores the values needed to calculate the lags used to predict the next step after the training data.
  • max_lag (int) — Maximum value of lag included in lags.
  • out_sample_residuals (np.ndarray) — Residuals of the model when predicting non training data. Only stored up to 1000 values.
  • regressor (regressor compatible with the scikit-learn API) — An instance of a regressor compatible with the scikit-learn API.
Methods
  • __repr__() (str) — Information displayed when a ForecasterAutoreg object is printed.
  • create_lags(y) (X_data : 2D np.ndarray, shape (samples, len(self.lags))) — Transforms a time series into a 2D array and a 1D array where each value of y is associated with the lags that precede it.
  • create_train_X_y(y, exog) (X_train : 2D np.ndarray, shape (len(y) - self.max_lag, len(self.lags))) — Create training matrices X, y
  • fit(y, exog) (self : ForecasterAutoreg) — Training ForecasterAutoreg
  • get_coef() (coef : 1D np.ndarray) — Return estimated coefficients for the linear regression model stored in the forecaster. Only valid when the forecaster has been trained using as regressor:LinearRegression(),Lasso()orRidge()`.
  • get_feature_importances() (feature_importances : 1D np.ndarray) — Return impurity-based feature importances of the model stored in the forecaster. Only valid when the forecaster has been trained using regressor=GradientBoostingRegressor() or regressor=RandomForestRegressor.
  • predict(steps, last_window, exog) (predictions : 1D np.array, shape (steps,)) — Iterative process in which, each prediction, is used as a predictor for the next step.
  • predict_interval(steps, last_window, exog, interval, n_boot, in_sample_residuals) (predictions : np.array, shape (steps, 3)) — Iterative process in which, each prediction, is used as a predictor for the next step and bootstrapping is used to estimate prediction intervals. Both, predictions and intervals, are returned.
  • set_lags(lags) (self) — Set new value to the attribute lags. Attribute max_lag is also updated.
  • set_out_sample_residuals(residuals) (self) — 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.
  • set_params(**params) (self) — Set new values to the parameters of the scikit learn model stored in the ForecasterAutoreg.
method
__repr__() → str

Information displayed when a ForecasterAutoreg object is printed.

method
create_lags(y)

Transforms a time series into a 2D array and a 1D array where each value of y is associated with 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 np.ndarray, pd.Series) — Training time series.
Returns (X_data : 2D np.ndarray, shape (samples, len(self.lags)))

2D array with the lag values (predictors).

ta : 1D np.ndarray, shape (nº observaciones - max(seld.lags),) Values of the time series related to each row of X_data.

method
create_train_X_y(y, exog=None)

Create training matrices X, y

Parameters
  • y (1D np.ndarray, pd.Series) — Training time series.
  • exog (np.ndarray, pd.Series, pd.DataFrame, default `None`) — Exogenous variable/s included as predictor/s. Must have the same number of observations as y and should be aligned so that y[i] is regressed on exog[i].
Returns (X_train : 2D np.ndarray, shape (len(y) - self.max_lag, len(self.lags)))

2D array with the training values (predictors).

ain : 1D np.ndarray, shape (len(y) - self.max_lag,) Values (target) of the time series related to each row of X_train.

method
fit(y, exog=None)

Training ForecasterAutoreg

Parameters
  • y (1D np.ndarray, pd.Series) — Training time series.
  • exog (np.ndarray, pd.Series, pd.DataFrame, default `None`) — Exogenous variable/s included as predictor/s. Must have the same number of observations as y and should be aligned so that y[i] is regressed on exog[i].
Returns (self : ForecasterAutoreg)

Trained ForecasterAutoreg

method
predict(steps, last_window=None, exog=None)

Iterative process in which, each prediction, is used as a predictor for the next step.

Returns (predictions : 1D np.array, shape (steps,))

Values predicted.

method
predict_interval(steps, last_window=None, exog=None, interval=[5, 95], n_boot=500, in_sample_residuals=True)

Iterative process in which, each prediction, is used as a predictor for the next step and bootstrapping is used to estimate prediction intervals. Both, predictions and intervals, are returned.

Returns (predictions : np.array, shape (steps, 3))

Values predicted by the forecaster and their estimated interval. Column 0 = predictions Column 1 = lower bound interval Column 2 = upper bound 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.

method
set_params(**params)

Set new values to the parameters of the scikit learn model stored in the ForecasterAutoreg.

Parameters
  • params (dict) — Parameters values.
method
set_lags(lags)

Set new value to the attribute lags. Attribute max_lag is also updated.

Parameters
  • 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.array: include only lags present in lags.
method
set_out_sample_residuals(residuals)

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
  • params (1D np.ndarray) — Values of residuals. If len(residuals) > 1000, only a random sample of 1000 values are stored.
method
get_coef()

Return estimated coefficients for the linear regression model stored in the forecaster. Only valid when the forecaster has been trained using as regressor:LinearRegression(),Lasso()orRidge()`.

Returns (coef : 1D np.ndarray)

Value of the coefficients associated with each predictor (lag). Coefficients are aligned so that coef[i] is the value associated with self.lags[i].

method
get_feature_importances()

Return impurity-based feature importances of the model stored in the forecaster. Only valid when the forecaster has been trained using regressor=GradientBoostingRegressor() or regressor=RandomForestRegressor.

Returns (feature_importances : 1D np.ndarray)

Impurity-based feature importances associated with each predictor (lag). Values are aligned so that feature_importances[i] is the value associated with self.lags[i].