SimpleExpSmoothingModel

class SimpleExpSmoothingModel(initialization_method: str = 'estimated', initial_level: Optional[float] = None, smoothing_level: Optional[float] = None, **fit_kwargs)

Bases: etna.models.holt_winters.HoltWintersModel

Exponential smoothing etna model.

Restricted version of HoltWinters model.

Notes

We use statsmodels.tsa.holtwinters.ExponentialSmoothing model from statsmodels package. They implement statsmodels.tsa.holtwinters.SimpleExpSmoothing model as a restricted version of ExponentialSmoothing model.

This model supports in-sample and out-of-sample prediction decomposition. For in-sample decomposition, level component is obtained directly from the fitted model. For out-of-sample, it estimated using an analytical form of the prediction function.

Init Exponential smoothing model with given params.

Parameters

• initialization_method (str) –

  Method for initialize the recursions. One of:

  • None

  • ’estimated’

  • ’heuristic’

  • ’legacy-heuristic’

  • ’known’

  None defaults to the pre-0.12 behavior where initial values are passed as part of fit. If any of the other values are passed, then the initial values must also be set when constructing the model. If ‘known’ initialization is used, then initial_level must be passed, as well as initial_trend and initial_seasonal if applicable. Default is ‘estimated’. “legacy-heuristic” uses the same values that were used in statsmodels 0.11 and earlier.

• initial_level (Optional[float]) – The initial level component. Required if estimation method is “known”. If set using either “estimated” or “heuristic” this value is used. This allows one or more of the initial values to be set while deferring to the heuristic for others or estimating the unset parameters.

• smoothing_level (Optional[float]) – The alpha value of the simple exponential smoothing, if the value is set then this value will be used as the value.

• fit_kwargs – Additional parameters for calling statsmodels.tsa.holtwinters.ExponentialSmoothing.fit().

Inherited-members

Methods

fit(ts)	Fit model.
forecast(ts[, return_components])	Make predictions.
get_model()	Get internal models that are used inside etna class.
load(path)	Load an object.
predict(ts[, return_components])	Make predictions with using true values as autoregression context if possible (teacher forcing).
save(path)	Save the object.
set_params(**params)	Return new object instance with modified parameters.
to_dict()	Collect all information about etna object in dict.

Attributes

context_size

Context size of the model.