"""
AutoCES
-------
"""
from typing import Optional
from statsforecast.models import AutoCES as SFAutoCES
from darts.models.forecasting.sf_model import StatsForecastModel
[docs]class AutoCES(StatsForecastModel):
def __init__(
self,
*args,
add_encoders: Optional[dict] = None,
quantiles: Optional[list[float]] = None,
random_state: Optional[int] = None,
**kwargs,
):
"""Auto-CES based on the `Statsforecasts package <https://github.com/Nixtla/statsforecast>`_.
Automatically selects the best Complex Exponential Smoothing (CES) model using an information criterion.
We refer to the `StatsForecast documentation
<https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autoces>`_ for the exhaustive documentation
of the arguments.
In addition to univariate deterministic forecasting, it comes with additional support:
- **Future covariates:** Use exogenous features to potentially improve predictive accuracy.
Darts adds support by first regressing the series against the future covariates using a
:class:`~darts.models.forecasting.linear_regression_model.LinearRegressionModel` model and then running the
StatsForecast model on the in-sample residuals from this original regression. This approach was inspired by
`this post of Stephan Kolassa <https://stats.stackexchange.com/q/220885>`_.
- **Probabilstic forecasting:** To generate probabilistic forecasts, you can set the following
parameters when calling :meth:`~darts.models.forecasting.sf_model.StatsForecastModel.predict`:
- Forecast quantile values directly by setting `predict_likelihood_parameters=True`.
- Generate sampled forecasts from these quantiles by setting `num_samples >> 1`.
- **Conformal prediction:** In addition to the native probabilistic support, you can perform conformal
prediction / forecasting by setting `prediction_intervals` at model creation. Then predict the in the same
way as described above.
- **Transferable series forecasting:** Apply the fitted model to a new input `series` at prediction time.
.. note::
Future covariates are not supported when the input series contain missing values.
.. note::
The first model call might take more time than all subsequent calls as the model relies on Numba and jit
compilation.
Parameters
----------
args
Positional arguments for ``statsforecasts.models.AutoCES``.
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'
}
..
quantiles
Optionally, produce quantile predictions at `quantiles` levels when performing probabilistic forecasting
with `num_samples > 1` or `predict_likelihood_parameters=True`.
random_state
Controls the randomness for reproducible forecasting.
kwargs
Keyword arguments for ``statsforecasts.models.AutoCES``.
Examples
--------
>>> from darts.datasets import AirPassengersDataset
>>> from darts.models import AutoCES
>>> from darts.utils.timeseries_generation import datetime_attribute_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 AutoCES parameters
>>> model = AutoCES(season_length=12, model="Z")
>>> model.fit(series, future_covariates=future_cov)
>>> pred = model.predict(6, future_covariates=future_cov)
>>> pred.values()
array([[437.52763596],
[412.76187406],
[445.26244666],
[498.15901335],
[492.5184186 ],
[550.25118939]])
"""
super().__init__(
model=SFAutoCES(*args, **kwargs),
quantiles=quantiles,
add_encoders=add_encoders,
random_state=random_state,
)