"""
Croston method
--------------
"""
from statsforecast.models import TSB as CrostonTSB
from statsforecast.models import CrostonClassic, CrostonOptimized, CrostonSBA
from darts.logging import raise_if, raise_if_not
from darts.models.forecasting.forecasting_model import (
LocalForecastingModel,
)
from darts.timeseries import TimeSeries
[docs]class Croston(LocalForecastingModel):
def __init__(
self,
version: str = "classic",
alpha_d: float = None,
alpha_p: float = None,
):
"""An implementation of the `Croston method
<https://otexts.com/fpp3/counts.html>`_ for intermittent
count series.
Relying on the implementation of `Statsforecasts package
<https://github.com/Nixtla/statsforecast>`_.
Parameters
----------
version
- "classic" corresponds to classic Croston.
- "optimized" corresponds to optimized classic Croston, which searches
for the optimal ``alpha`` smoothing parameter and can take longer
to run. Otherwise, a fixed value of ``alpha=0.1`` is used.
- "sba" corresponds to the adjustment of the Croston method known as
the Syntetos-Boylan Approximation [1]_.
- "tsb" corresponds to the adjustment of the Croston method proposed by
Teunter, Syntetos and Babai [2]_. In this case, `alpha_d` and `alpha_p` must
be set.
alpha_d
For the "tsb" version, the alpha smoothing parameter to apply on demand.
alpha_p
For the "tsb" version, the alpha smoothing parameter to apply on probability.
References
----------
.. [1] Aris A. Syntetos and John E. Boylan. The accuracy of intermittent demand estimates.
International Journal of Forecasting, 21(2):303 – 314, 2005.
.. [2] Ruud H. Teunter, Aris A. Syntetos, and M. Zied Babai.
Intermittent demand: Linking forecasting to inventory obsolescence.
European Journal of Operational Research, 214(3):606 – 615, 2011.
Examples
--------
>>> from darts.datasets import AirPassengersDataset
>>> from darts.models import Croston
>>> series = AirPassengersDataset().load()
>>> # use the optimized version to automatically select best alpha parameter
>>> model = Croston(version="optimized")
>>> model.fit(series)
>>> pred = model.predict(6)
>>> pred.values()
array([[461.7666],
[461.7666],
[461.7666],
[461.7666],
[461.7666],
[461.7666]])
"""
super().__init__(add_encoders=None)
raise_if_not(
version.lower() in ["classic", "optimized", "sba", "tsb"],
'The provided "version" parameter must be set to "classic", "optimized", "sba" or "tsb".',
)
if version == "classic":
self.model = CrostonClassic()
elif version == "optimized":
self.model = CrostonOptimized()
elif version == "sba":
self.model = CrostonSBA()
else:
raise_if(
alpha_d is None or alpha_p is None,
'alpha_d and alpha_p must be specified when using "tsb".',
)
self.alpha_d = alpha_d
self.alpha_p = alpha_p
self.model = CrostonTSB(alpha_d=self.alpha_d, alpha_p=self.alpha_p)
self.version = version
@property
def supports_multivariate(self) -> bool:
return False
[docs] def fit(self, series: TimeSeries):
super().fit(series)
self._assert_univariate(series)
series = self.training_series
self.model.fit(
y=series.values(copy=False).flatten(),
# X can be used to passe future covariates only when conformal prediction is used
X=None,
)
return self
[docs] def predict(
self,
n: int,
num_samples: int = 1,
verbose: bool = False,
):
super().predict(n, num_samples)
values = self.model.predict(
h=n,
# X can be used to passe future covariates only when conformal prediction is used
X=None,
)["mean"]
return self._build_forecast_series(values)
@property
def min_train_series_length(self) -> int:
return 10
@property
def _supports_range_index(self) -> bool:
return True