etna.models.HoltWintersModel#

class HoltWintersModel(trend: str | None = None, damped_trend: bool = False, seasonal: str | None = None, seasonal_periods: int | None = None, initialization_method: str = 'estimated', initial_level: float | None = None, initial_trend: float | None = None, initial_seasonal: Sequence[float] | None = None, use_boxcox: bool | str | float = False, bounds: Dict[str, Tuple[float, float]] | None = None, dates: Sequence[datetime] | None = None, freq: str | None = None, missing: str = 'none', smoothing_level: float | None = None, smoothing_trend: float | None = None, smoothing_seasonal: float | None = None, damping_trend: float | None = None, **fit_kwargs)[source]#

Bases: PerSegmentModelMixin, NonPredictionIntervalContextIgnorantModelMixin, NonPredictionIntervalContextIgnorantAbstractModel

Holt-Winters’ etna model.

This model corresponds to statsmodels.tsa.holtwinters.ExponentialSmoothing.

Notes

The model statsmodels.tsa.holtwinters.ExponentialSmoothing is used in the implementation.

This model supports in-sample and out-of-sample prediction decomposition. Prediction components for Holt-Winters model are: level, trend and seasonality. 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-Winters’ model with given params.

Parameters:

  • trend (str | None) –

    Type of trend component. One of:

    • ’add’

    • ’mul’

    • ’additive’

    • ’multiplicative’

    • None

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

  • seasonal (str | None) –

    Type of seasonal component. One of:

    • ’add’

    • ’mul’

    • ’additive’

    • ’multiplicative’

    • None

  • seasonal_periods (int | None) – The number of periods in a complete seasonal cycle, e.g., 4 for quarterly data or 7 for daily data with a weekly cycle.

  • 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 (float | None) – 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 (float | None) – 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.

  • initial_seasonal (Sequence[float] | None) – The initial seasonal component. An array of length seasonal or length seasonal - 1 (in which case the last initial value is computed to make the average effect zero). Only used if initialization is ‘known’. 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.

  • use_boxcox ({True, False, 'log', float}, optional) –

    Should the Box-Cox transform be applied to the data first? One of:

    • True

    • False

    • ’log’: apply log

    • float: lambda value

  • bounds (Dict[str, Tuple[float, float]] | None) – An dictionary containing bounds for the parameters in the model, excluding the initial values if estimated. The keys of the dictionary are the variable names, e.g., smoothing_level or initial_slope. The initial seasonal variables are labeled initial_seasonal.<j> for j=0,…,m-1 where m is the number of period in a full season. Use None to indicate a non-binding constraint, e.g., (0, None) constrains a parameter to be non-negative.

  • dates (Sequence[datetime] | None) – An array-like object of datetime objects. If a Pandas object is given for endog, it is assumed to have a DateIndex.

  • freq (str | None) – The frequency of the time-series. A Pandas offset or ‘B’, ‘D’, ‘W’, ‘M’, ‘A’, or ‘Q’. This is optional if dates are given.

  • missing (str) – Available options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none’.

  • smoothing_level (float | None) – The alpha value of the simple exponential smoothing, if the value is set then this value will be used as the value.

  • smoothing_trend (float | None) – The beta value of the Holt’s trend method, if the value is set then this value will be used as the value.

  • smoothing_seasonal (float | None) – The gamma value of the holt winters seasonal method, if the value is set then this value will be used as the value.

  • damping_trend (float | None) – 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().

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

This class stores its __init__ parameters as attributes.

context_size

Context size of the model.
fit(ts: TSDataset) → PerSegmentModelMixin[source]#

Fit model.

Parameters:

ts (TSDataset) – Dataset with features

Returns:

Model after fit

Return type:

PerSegmentModelMixin

forecast(ts: TSDataset, return_components: bool = False) → TSDataset[source]#

Make predictions.

Parameters:

  • ts (TSDataset) – Dataset with features

  • return_components (bool) – If True additionally returns forecast components

Returns:

Dataset with predictions

Return type:

TSDataset

get_model() → Dict[str, Any][source]#

Get internal models that are used inside etna class.

Internal model is a model that is used inside etna to forecast segments, e.g. catboost.CatBoostRegressor or sklearn.linear_model.Ridge.

Returns:

dictionary where key is segment and value is internal model

Return type:

Dict[str, Any]

classmethod load(path: Path) → Self[source]#

Load an object.

Warning

This method uses dill module which is not secure. It is possible to construct malicious data which will execute arbitrary code during loading. Never load data that could have come from an untrusted source, or that could have been tampered with.

Parameters:

path (Path) – Path to load object from.

Returns:

Loaded object.

Return type:

Self

params_to_tune() → Dict[str, BaseDistribution][source]#

Get default grid for tuning hyperparameters.

This grid tunes parameters: trend, damped_trend, use_boxcox. If self.seasonal is not None, then it also tunes seasonal parameter. Other parameters are expected to be set by the user.

Returns:

Grid to tune.

Return type:

Dict[str, BaseDistribution]

predict(ts: TSDataset, return_components: bool = False) → TSDataset[source]#

Make predictions with using true values as autoregression context if possible (teacher forcing).

Parameters:

  • ts (TSDataset) – Dataset with features

  • return_components (bool) – If True additionally returns prediction components

Returns:

Dataset with predictions

Return type:

TSDataset

save(path: Path)[source]#

Save the object.

Parameters:

path (Path) – Path to save object to.

set_params(**params: dict) → Self[source]#

Return new object instance with modified parameters.

Method also allows to change parameters of nested objects within the current object. For example, it is possible to change parameters of a model in a Pipeline.

Nested parameters are expected to be in a <component_1>.<...>.<parameter> form, where components are separated by a dot.

Parameters:

**params (dict) – Estimator parameters

Returns:

New instance with changed parameters

Return type:

Self

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = NaiveModel(lag=1)
>>> transforms = [AddConstTransform(in_column="target", value=1)]
>>> pipeline = Pipeline(model, transforms=transforms, horizon=3)
>>> pipeline.set_params(**{"model.lag": 3, "transforms.0.value": 2})
Pipeline(model = NaiveModel(lag = 3, ), transforms = [AddConstTransform(in_column = 'target', value = 2, inplace = True, out_column = None, )], horizon = 3, )
to_dict()[source]#

Collect all information about etna object in dict.

property context_size: int[source]#

Context size of the model. Determines how many history points do we ask to pass to the model.

Zero for this model.