model_selection_statsmodels

function

skforecast.model_selection_statsmodels.backtesting_autoreg_statsmodels(y, lags, initial_train_size, steps, metric, exog=None, verbose=False)

Backtesting (validation) of AutoReg model from statsmodels v0.12. The model is trained only once using the initial_train_size first observations. In each iteration, a number of steps predictions are evaluated. This evaluation is much faster than cross-validation since the model is trained only once.

Returns (y)

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y .

function

skforecast.model_selection_statsmodels.cv_autoreg_statsmodels(y, lags, initial_train_size, steps, metric, exog=None, allow_incomplete_fold=True, verbose=False)

a .

Returns (y)

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y .

function

skforecast.model_selection_statsmodels.backtesting_sarimax_statsmodels(y, initial_train_size, steps, metric, order=(1, 0, 0), seasonal_order=(0, 0, 0, 0), trend=None, alpha=0.05, exog=None, sarimax_kwargs={}, fit_kwargs={'disp': 0}, verbose=False)

Backtesting (validation) of SARIMAX model from statsmodels v0.12. The model is trained only once using the initial_train_size first observations. In each iteration, a number of steps predictions are evaluated. This evaluation is much faster than cross-validation since the model is trained only once.

https://www.statsmodels.org/dev/examples/notebooks/generated/statespace_forecasting.html

Returns ())

.

y . s l .

function

skforecast.model_selection_statsmodels.cv_sarimax_statsmodels(y, initial_train_size, steps, metric, order=(1, 0, 0), seasonal_order=(0, 0, 0, 0), trend=None, alpha=0.05, exog=None, allow_incomplete_fold=True, sarimax_kwargs={}, fit_kwargs={'disp': 0}, verbose=False)

a .

Returns (y)

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y . s l .

function

skforecast.model_selection_statsmodels.grid_search_sarimax_statsmodels(y, param_grid, initial_train_size, steps, metric, exog=None, method='cv', allow_incomplete_fold=True, sarimax_kwargs={}, fit_kwargs={'disp': 0}, verbose=False)

Exhaustive search over specified parameter values for a SARIMAX model from statsmodels v0.12. Validation is done using time series cross-validation or backtesting.

Returns (e)

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