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Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
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rtavenar
reviewed
May 18, 2026
rtavenar
reviewed
May 18, 2026
rtavenar
reviewed
May 18, 2026
…sonality before stationarity check
…r speed. Add max_iter for AutoVARIMA. Black formating.
rtavenar
reviewed
Jun 25, 2026
| # * :math:`\epsilon_{t-k}` are lagged errors | ||
| # * :math:`\epsilon_t` is white noise | ||
| # | ||
| # Fitting the model with data estimates the parameters :math:`c, \phi_1, ... \phi_p, \theta1, ..., \theta_q` through |
rtavenar
reviewed
Jun 25, 2026
| # forecasted values depends on the last two values. Hence, forecasting with a horizon :math:`n > 1` | ||
| # will use computed values for :math:`n-1, ..., 1`. | ||
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| from sklearn.metrics import mean_absolute_error |
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Use our own mae from the other PR?
rtavenar
reviewed
Jun 25, 2026
| # The AutoVARIMA model provides a way to automatically select the hyperparameters of the VARIMA model | ||
| # based on the training data. Selection of the order of differentiation :math:`d` aims at applying VARMA modeling onto | ||
| # stationary data whereas selection of :math:`p` and :math:`q` orders is driven by the minimization of AIC for | ||
| # relative VARMA models. |
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Add VARIMA and AutoVARIMA models for forecasting.
Supports naive seasonality.
Supports variable length TS.
VARIMA uses scipy nelder-mead optimization when q>0
AutoVARIMA uses statsmodels kpss tests for stationarity
Based on https://otexts.com/fpp3/
TODO: