HoltModel

class HoltModel(exponential: bool = False, damped_trend: bool = False, initialization_method: str = 'estimated', initial_level: Optional[float] = None, initial_trend: Optional[float] = None, smoothing_level: Optional[float] = None, smoothing_trend: Optional[float] = None, damping_trend: Optional[float] = None, **fit_kwargs)

Bases: etna.models.mixins.PerSegmentModelMixin, etna.models.mixins.NonPredictionIntervalContextIgnorantModelMixin, etna.models.base.NonPredictionIntervalContextIgnorantAbstractModel

Holt etna model.

This is a restricted version of HoltWintersModel. And it corresponds to statsmodels.tsa.holtwinters.Holt.

Notes

The model statsmodels.tsa.holtwinters.ExponentialSmoothing is used in the implementation. In statsmodels package the model statsmodels.tsa.holtwinters.Holt is implemented as a restricted version of statsmodels.tsa.holtwinters.ExponentialSmoothing model.

This model supports in-sample and out-of-sample prediction decomposition. Prediction components for Holt model are: level and trend. For in-sample decomposition, components are obtained directly from the fitted model. For out-of-sample, components estimated using an analytical form of the prediction function.

Init Holt model with given params.

Parameters

• exponential (bool) –

  Type of trend component. One of:

  • False: additive trend

  • True: multiplicative trend

• damped_trend (bool) – Should the trend component be damped.

• 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.

• initial_trend (Optional[float]) – The initial trend 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.

• smoothing_trend (Optional[float]) – The beta value of the Holt’s trend method, if the value is set then this value will be used as the value.

• damping_trend (Optional[float]) – The phi value of the damped method, 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.
params_to_tune()	Get default grid for tuning hyperparameters.
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.

params_to_tune() → Dict[str, etna.distributions.distributions.BaseDistribution]

Get default grid for tuning hyperparameters.

Returns

Grid to tune.

Return type

Dict[str, etna.distributions.distributions.BaseDistribution]