class AutoARIMA(ExtendedForecastingModel):
def __init__(self, *autoarima_args, **autoarima_kwargs):
""" Auto-ARIMA
This implementation is a thin wrapper around `pmdarima.txt 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.txt AutoARIMA model.
See `pmdarima.txt documentation
<https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.AutoARIMA.html>`_
for an extensive documentation and a list of supported parameters.
Parameters
----------
autoarima_args
Positional arguments for the pmdarima.txt AutoARIMA model
autoarima_kwargs
Keyword arguments for the pmdarima.txt AutoARIMA model
"""
super().__init__()
self.model = PmdAutoARIMA(*autoarima_args, **autoarima_kwargs)
self.trend = self.model.trend
def __str__(self):
return 'Auto-ARIMA'
def fit(self, series: TimeSeries, exog: Optional[TimeSeries] = None):
super().fit(series, exog)
series = self.training_series
self.model.fit(series.values(), X=exog.values() if exog else None)
def predict(self,
n: int,
exog: Optional[TimeSeries] = None,
num_samples: int = 1):
super().predict(n, exog, num_samples)
forecast = self.model.predict(n_periods=n,
X=exog.values() if exog else None)
return self._build_forecast_series(forecast)
@property
def min_train_series_length(self) -> int:
return 30
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
class AutoARIMA(UnivariateForecastingModel):
def __init__(self, *autoarima_args, **autoarima_kwargs):
""" Auto-ARIMA
This implementation is a thin wrapper around `pmdarima.txt 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.txt AutoARIMA model.
See `pmdarima.txt documentation
<https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.AutoARIMA.html>`_
for an extensive documentation and a list of supported parameters.
Parameters
----------
autoarima_args
Positional arguments for the pmdarima.txt AutoARIMA model
autoarima_kwargs
Keyword arguments for the pmdarima.txt AutoARIMA model
"""
super().__init__()
self.model = PmdAutoARIMA(*autoarima_args, **autoarima_kwargs)
def __str__(self):
return 'Auto-ARIMA'
def fit(self, series: TimeSeries):
super().fit(series)
series = self.training_series
self.model.fit(series.values())
def predict(self, n):
super().predict(n)
forecast = self.model.predict(n_periods=n)
return self._build_forecast_series(forecast)
@property
def min_train_series_length(self) -> int:
return 30
class AutoARIMA(DualCovariatesForecastingModel):
def __init__(self, *autoarima_args, **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
"""
super().__init__()
self.model = PmdAutoARIMA(*autoarima_args, **autoarima_kwargs)
self.trend = self.model.trend
def __str__(self):
return "Auto-ARIMA"
def _fit(self,
series: TimeSeries,
future_covariates: Optional[TimeSeries] = None):
super()._fit(series, future_covariates)
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,
):
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
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