Comments (1)
Hi @tyg3rr,
Thanks for using skforecast.
Unfortunately, Sarimax models can become slower to fit when adding a seasonal period, especially if the seasonality is strong or the dataset is large. The auto-arima process may be a faster alternative compare to the exhaustive grid search (although the fitting process is the same), see an example here: https://cienciadedatos.net/documentos/py51-arima-sarimax-models-python#Model-tuning
Thanks!
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Related Issues (20)
- `ForecasterAutoreg` fails to fit when `exog` do not have string column names HOT 1
- `ForecasterAutoreg` fails to fit when index do not start from 0 HOT 3
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- Back testing HOT 1
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- Naming convention for backtesting methods HOT 2
- Feature request regarding time series with different lengths HOT 5
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- Feature request: Recursive multistep multivariate forecasting and direct multistep multi-series forecasting HOT 4
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- Issue saving ForecasterSarimax object HOT 4
- Custom predictors are inefficient for window features HOT 1
- Feature request: Add ability to skip steps in backtesting HOT 6
- Bad error handling when Index is neither RangeIndex nor DateIndex HOT 3
- Why do i have faster runtime when using more frequent refits HOT 2
- syntax error !! HOT 1
- Feature request: Custom predictors for multivariate forecasting
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