SimpleExpSmoothingModel

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

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

Exponential smoothing etna model.

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

Notes

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

params_to_tune()

Get 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.

fit(ts: etna.datasets.tsdataset.TSDataset) etna.models.mixins.PerSegmentModelMixin

Fit model.

Parameters

ts (etna.datasets.tsdataset.TSDataset) – Dataset with features

Returns

Model after fit

Return type

etna.models.mixins.PerSegmentModelMixin

forecast(ts: etna.datasets.tsdataset.TSDataset, return_components: bool = False) etna.datasets.tsdataset.TSDataset

Make predictions.

Parameters
Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

get_model() Dict[str, Any]

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: pathlib.Path) typing_extensions.Self

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 (pathlib.Path) – Path to load object from.

Returns

Loaded object.

Return type

typing_extensions.Self

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

Get grid for tuning hyperparameters.

This is default implementation with empty grid.

Returns

Empty grid.

Return type

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

predict(ts: etna.datasets.tsdataset.TSDataset, return_components: bool = False) etna.datasets.tsdataset.TSDataset

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

Parameters
Returns

Dataset with predictions

Return type

etna.datasets.tsdataset.TSDataset

save(path: pathlib.Path)

Save the object.

Parameters

path (pathlib.Path) – Path to save object to.

set_params(**params: dict) etna.core.mixins.TMixin

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 – Estimator parameters

  • self (etna.core.mixins.TMixin) –

  • params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = 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()

Collect all information about etna object in dict.

property context_size: int

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

Zero for this model.