"""
AutoMFLES
-----------
"""
from typing import Optional
from statsforecast.models import AutoMFLES as SFAutoMFLES
from darts.logging import get_logger
from darts.models.forecasting.sf_model import StatsForecastModel
logger = get_logger(__name__)
[docs]class AutoMFLES(StatsForecastModel):
def __init__(
self,
*args,
add_encoders: Optional[dict] = None,
quantiles: Optional[list[float]] = None,
random_state: Optional[int] = None,
**kwargs,
):
"""Auto-MFLES based on the `Statsforecasts package <https://github.com/Nixtla/statsforecast>`_.
Automatically selects the best MFLES model from all feasible combinations of the parameters
`seasonality_weights`, `smoother`, `ma`, and `seasonal_period`. Selection is made using the sMAPE metric by
default. We refer to the `StatsForecast documentation
<https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#automfles>`_ 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.
- **Probabilstic / Conformal forecasting:** Probabilstic forecasting can be performed using conformal
prediction. To activate it, simply set `prediction_intervals` at model creation. 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`.
- **Transferable series forecasting:** Apply the fitted model to a new input `series` at prediction time.
Darts adds support by first fitting a copy of the model on the new series, and then using that model to
generate the corresponding forecast.
.. 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.AutoMFLES``.
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
Control the randomness of probabilistic conformal forecasts (sample generation) across different runs.
kwargs
Keyword arguments for ``statsforecasts.models.AutoMFLES``.
Examples
--------
>>> from darts.datasets import AirPassengersDataset
>>> from darts.models import AutoMFLES
>>> 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 AutoMFLES parameters
>>> model = AutoMFLES(season_length=12, test_size=12)
>>> model.fit(series, future_covariates=future_cov)
>>> pred = model.predict(6, future_covariates=future_cov)
>>> pred.values()
array([[466.03298745],
[450.76192105],
[517.6342497 ],
[511.62988828],
[520.15305998],
[593.38690019]])
"""
super().__init__(
model=SFAutoMFLES(*args, **kwargs),
quantiles=quantiles,
add_encoders=add_encoders,
random_state=random_state,
)
@property
def _supports_native_future_covariates(self) -> bool:
# StatsForecast didn't set the `use_exog=True` flag for AutoMFLES even though it supports it.
return True