Source code for darts.models.forecasting.auto_arima


from typing import Optional

from pmdarima import AutoARIMA as PmdAutoARIMA

from darts.logging import get_logger, raise_if
from darts.models.forecasting.forecasting_model import (
from darts.timeseries import TimeSeries

logger = get_logger(__name__)

[docs]class AutoARIMA(FutureCovariatesLocalForecastingModel): def __init__( self, *autoarima_args, add_encoders: Optional[dict] = None, **autoarima_kwargs ): """Auto-ARIMA This implementation is a thin wrapper around `pmdarima AutoARIMA model <>`_, which provides functionality similar to R's `auto.arima <>`_. This model supports the same parameters as the pmdarima AutoARIMA model. See `pmdarima documentation <>`_ for an extensive documentation and a list of supported parameters. .. note:: For a faster and probabilistic version of AutoARIMA, checkout the :class:`StatsForecastAutoARIMA` model. Parameters ---------- autoarima_args Positional arguments for the pmdarima.AutoARIMA model autoarima_kwargs Keyword arguments for the pmdarima.AutoARIMA model add_encoders A large number of future covariates can be automatically generated with `add_encoders`. This can be done by adding multiple pre-defined index encoders and/or custom user-made functions that will be used as index encoders. Additionally, a transformer such as Darts' :class:`Scaler` can be added to transform the generated covariates. This happens all under one hood and only needs to be specified at model creation. Read :meth:`SequentialEncoder <darts.dataprocessing.encoders.SequentialEncoder>` to find out more about ``add_encoders``. Default: ``None``. An example showing some of ``add_encoders`` features: .. highlight:: python .. code-block:: python def encode_year(idx): return (idx.year - 1950) / 50 add_encoders={ 'cyclic': {'future': ['month']}, 'datetime_attribute': {'future': ['hour', 'dayofweek']}, 'position': {'future': ['relative']}, 'custom': {'future': [encode_year]}, 'transformer': Scaler(), 'tz': 'CET' } .. Examples -------- >>> from darts.datasets import AirPassengersDataset >>> from darts.models import AutoARIMA >>> from darts.utils.timeseries_generation import holidays_timeseries >>> series = AirPassengersDataset().load() >>> # optionally, use some future covariates; e.g. the value of the month encoded as a sine and cosine series >>> future_cov = datetime_attribute_timeseries(series, "month", cyclic=True, add_length=6) >>> # define some boundaries for the parameters >>> model = AutoARIMA(start_p=8, max_p=12, start_q=1) >>>, future_covariates=future_cov) >>> pred = model.predict(6, future_covariates=future_cov) >>> pred.values() array([[449.79716178], [416.31180633], [445.28005229], [485.27121314], [507.61787454], [561.26993332]]) """ super().__init__(add_encoders=add_encoders) self.model = PmdAutoARIMA(*autoarima_args, **autoarima_kwargs) self.trend = self.model.trend @property def supports_multivariate(self) -> bool: return False def _fit(self, series: TimeSeries, future_covariates: Optional[TimeSeries] = None): super()._fit(series, future_covariates) self._assert_univariate(series) series = self.training_series series.values(), X=future_covariates.values() if future_covariates else None ) return self def _predict( self, n: int, future_covariates: Optional[TimeSeries] = None, num_samples: int = 1, verbose: bool = False, ): super()._predict(n, future_covariates, num_samples) forecast = self.model.predict( n_periods=n, X=future_covariates.values() if future_covariates else None ) return self._build_forecast_series(forecast) @property def min_train_series_length(self) -> int: return 10 @property def _supports_range_index(self) -> bool: raise_if( self.trend and self.trend != "c", "'trend' is not None. Range indexing is not supported in that case.", logger, ) return True