LinearMultiSegmentModel

class LinearMultiSegmentModel(fit_intercept: bool = True, **kwargs)

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

Class holding sklearn.linear_model.LinearRegression for all segments.

Notes

Target components are formed as the terms from linear regression formula.

Create instance of LinearModel with given parameters.

Parameters

fit_intercept (bool) – Whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (i.e. data is expected to be centered).

Inherited-members

Methods

fit(ts)	Fit model.
forecast(ts[, return_components])	Make predictions.
get_model()	Get internal model that is 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]