StatsForecastAutoETS

class StatsForecastAutoETS(season_length: int = 1, model: str = 'ZZZ', damped: bool | None = None, phi: float | None = None)[source]

StatsForecast Automatic Exponential Smoothing model.

Direct interface to statsforecast.models.AutoETS, from statsforecast [1] by Nixtla. The statsforecast implementation is a mirror of Hyndman’s forecast::ets [2].

Automatically selects the best ETS (Error, Trend, Seasonality) model using an information criterion. Default is Akaike Information Criterion (AICc), while particular models are estimated using maximum likelihood. The state-space equations can be determined based on their $M$ multiplicative, $A$ additive, $Z$ optimized or $N$ omitted components. The model string parameter defines the ETS equations: E in [$M, A, Z$], T in [$N, A, M, Z$], and S in [$N, A, M, Z$].

For example when model=’ANN’ (additive error, no trend, and no seasonality), ETS will explore only a simple exponential smoothing.

If the component is selected as ‘Z’, it operates as a placeholder to ask the AutoETS model to figure out the best parameter.

Parameters:

season_lengthint, optional (default=1): Number of observations per unit of time. Ex: 24 Hourly data.
modelstr, optional (default=”ZZZ”): Controlling state-space-equations.
dampedbool, optional (default=None): A parameter that ‘dampens’ the trend.
phifloat, optional (default=None): Smoothing parameter for trend damping. Only used when damped=True.

Attributes:

cutoff: Cut-off = “present time” state of forecaster.
fh: Forecasting horizon that was passed.
is_fitted: Whether fit has been called.
state: State of the estimator.

References

[1]

https://nixtlaverse.nixtla.io/statsforecast/src/core/models.html#autoets

[2]

https://github.com/robjhyndman/forecast

Examples

>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.statsforecast import StatsForecastAutoETS
>>> y = load_airline()
>>> forecaster = StatsForecastAutoETS(
...     season_length=12, model="AAN", damped=True
... )
>>> forecaster.fit(y)
StatsForecastAutoETS(...)
>>> y_pred = forecaster.predict(fh=[1, 2, 3])

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(y[, X, fh])	Fit forecaster to training data.
`fit_predict`(y[, X, fh, X_pred])	Fit and forecast time series at future horizon.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_pretrained_params`([deep])	Get pretrained parameters of this estimator.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`([fh, X])	Forecast time series at future horizon.
`predict_interval`([fh, X, coverage])	Compute/return prediction interval forecasts.
`predict_proba`([fh, X, marginal])	Compute/return fully probabilistic forecasts.
`predict_quantiles`([fh, X, alpha])	Compute/return quantile forecasts.
`predict_residuals`([y, X])	Return residuals of time series forecasts.
`predict_var`([fh, X, cov])	Compute/return variance forecasts.
`pretrain`(y[, X, fh])	Pre-train forecaster on panel (global) data.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`score`(y[, X, fh])	Scores forecast against ground truth, using MAPE (non-symmetric).
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`update`(y[, X, update_params])	Update cutoff value and, optionally, fitted parameters.
`update_predict`(y[, cv, X, update_params, ...])	Make predictions and update model iteratively over the test set.
`update_predict_single`([y, fh, X, update_params])	Update model with new data and make forecasts.