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JoaquinAmatRodrigo avatar JoaquinAmatRodrigo commented on June 10, 2024

Hi @thec0dewriter,
Thanks for using skforecast.
Do you mean to extract the "one-step-ahead" predictions on the train data? Then this should help:

# Create Recursive multi-step forecaster (ForecasterAutoreg)
# ==============================================================================
forecaster = ForecasterAutoreg(
                 regressor = HistGradientBoostingRegressor(random_state=123),
                 lags      = 5
             )

forecaster.fit(
    y    = data['y'],
    exog = data[['exog_1', 'exog_2']]
)


# Training matrices used by the forecaster to fit the internal regressor
# ==============================================================================
X_train, y_train = forecaster.create_train_X_y(
                        y    = data['y'],
                        exog = data[['exog_1', 'exog_2']]
                    )
display(X_train.head(3))
display(y_train.head(3))

# Predict using the internal regressor
# ==============================================================================
forecaster.regressor.predict(X_tain)

from skforecast.

thec0dewriter avatar thec0dewriter commented on June 10, 2024

Hello @JoaquinAmatRodrigo ,
Yes, this is what I was looking for more or less.
Thanks for the help and the quick response.

from skforecast.

JavierEscobarOrtiz avatar JavierEscobarOrtiz commented on June 10, 2024

Hello @thec0dewriter,

Here is a similar issue:

#320

from skforecast.

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