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Imp/Add new model StatsForecastAutoTBATS (#2611)
* Add new model StatsForecastAutoTBATS * Update darts/models/forecasting/sf_auto_tbats.py Co-authored-by: Dennis Bader <[email protected]> * Update CHANGELOG * Update README.md * Update covariates.md * Update test_probabilistic_models.py * Update test_probabilistic_models.py * update changelog and readme --------- Co-authored-by: Dennis Bader <[email protected]>
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,104 @@ | ||
| """ | ||
| StatsForecastAutoTBATS | ||
| ----------- | ||
| """ | ||
|
|
||
| from statsforecast.models import AutoTBATS as SFAutoTBATS | ||
|
|
||
| from darts import TimeSeries | ||
| from darts.models.components.statsforecast_utils import ( | ||
| create_normal_samples, | ||
| one_sigma_rule, | ||
| unpack_sf_dict, | ||
| ) | ||
| from darts.models.forecasting.forecasting_model import LocalForecastingModel | ||
|
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|
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| class StatsForecastAutoTBATS(LocalForecastingModel): | ||
| def __init__(self, *autoTBATS_args, **autoTBATS_kwargs): | ||
| """Auto-TBATS based on `Statsforecasts package | ||
| <https://github.com/Nixtla/statsforecast>`_. | ||
| Automatically selects the best TBATS model from all feasible combinations of the parameters `use_boxcox`, | ||
| `use_trend`, `use_damped_trend`, and `use_arma_errors`. Selection is made using the AIC. | ||
| Default value for `use_arma_errors` is True since this enables the evaluation of models with | ||
| and without ARMA errors. | ||
| <https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=f3de25596ab60ef0e886366826bf58a02b35a44f> | ||
| <https://doi.org/10.4225/03/589299681de3d> | ||
| We refer to the `statsforecast AutoTBATS documentation | ||
| <https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autotbats>`_ | ||
| for the exhaustive documentation of the arguments. | ||
| Parameters | ||
| ---------- | ||
| autoTBATS_args | ||
| Positional arguments for ``statsforecasts.models.AutoTBATS``. | ||
| autoTBATS_kwargs | ||
| Keyword arguments for ``statsforecasts.models.AutoTBATS``. | ||
| Examples | ||
| -------- | ||
| >>> from darts.datasets import AirPassengersDataset | ||
| >>> from darts.models import StatsForecastAutoTBATS | ||
| >>> series = AirPassengersDataset().load() | ||
| >>> # define StatsForecastAutoTBATS parameters | ||
| >>> model = StatsForecastAutoTBATS(season_length=12) | ||
| >>> model.fit(series) | ||
| >>> pred = model.predict(6) | ||
| >>> pred.values() | ||
| array([[450.79653684], | ||
| [472.09265790], | ||
| [497.76948306], | ||
| [510.74927369], | ||
| [520.92224557], | ||
| [570.33881522]]) | ||
| """ | ||
| super().__init__() | ||
| self.model = SFAutoTBATS(*autoTBATS_args, **autoTBATS_kwargs) | ||
|
|
||
| def fit(self, series: TimeSeries): | ||
| super().fit(series) | ||
| self._assert_univariate(series) | ||
| series = self.training_series | ||
| self.model.fit( | ||
| series.values(copy=False).flatten(), | ||
| ) | ||
| return self | ||
|
|
||
| def predict( | ||
| self, | ||
| n: int, | ||
| num_samples: int = 1, | ||
| verbose: bool = False, | ||
| show_warnings: bool = True, | ||
| ): | ||
| super().predict(n, num_samples) | ||
| forecast_dict = self.model.predict( | ||
| h=n, | ||
| level=(one_sigma_rule,), # ask one std for the confidence interval. | ||
| ) | ||
|
|
||
| mu, std = unpack_sf_dict(forecast_dict) | ||
| if num_samples > 1: | ||
| samples = create_normal_samples(mu, std, num_samples, n) | ||
| else: | ||
| samples = mu | ||
|
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| return self._build_forecast_series(samples) | ||
|
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||
| @property | ||
| def supports_multivariate(self) -> bool: | ||
| return False | ||
|
|
||
| @property | ||
| def min_train_series_length(self) -> int: | ||
| return 10 | ||
|
|
||
| @property | ||
| def _supports_range_index(self) -> bool: | ||
| return True | ||
|
|
||
| @property | ||
| def supports_probabilistic_prediction(self) -> bool: | ||
| return True |
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