"""
AutoARIMA
---------
"""
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 (
FutureCovariatesLocalForecastingModel,
)
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
<https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.AutoARIMA.html>`_,
which provides functionality similar to R's `auto.arima
<https://www.rdocumentation.org/packages/forecast/versions/7.3/topics/auto.arima>`_.
This model supports the same parameters as the pmdarima AutoARIMA model.
See `pmdarima documentation
<https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.AutoARIMA.html>`_
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)
>>> model.fit(series, 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
self.model.fit(
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