etna.transforms.ModelDecomposeTransform#

class ModelDecomposeTransform(model: NonPredictionIntervalContextIgnorantAbstractModel | NonPredictionIntervalContextRequiredAbstractModel | PredictionIntervalContextIgnorantAbstractModel | PredictionIntervalContextRequiredAbstractModel, in_column: str = 'target', residuals: bool = False)[source]#

Bases: IrreversibleTransform

Transform that uses ETNA models to estimate series decomposition.

Note

This transform decomposes only in-sample data. For the future timestamps it produces NaN. For the dataset to be transformed, it should contain at least the minimum amount of in-sample timestamps that are required by the model.

Init ModelDecomposeTransform.

Parameters:

model (NonPredictionIntervalContextIgnorantAbstractModel | NonPredictionIntervalContextRequiredAbstractModel | PredictionIntervalContextIgnorantAbstractModel | PredictionIntervalContextRequiredAbstractModel) –
instance of the model to use for the decomposition. Note that not all models are supported. Possible selections are:
- HoltWintersModel
- ProphetModel
- SARIMAXModel
- DeadlineMovingAverageModel
- SeasonalMovingAverageModel
- BATSModel
- TBATSModel
Currently, only the specified series itself is used for model fitting. There is no way to add additional features/regressors to the decomposition model.
in_column (str) – name of the processed column.
residuals (bool) – whether to add residuals after decomposition. This guarantees that all components, including residuals, sum up to the series.

Warning

Options for parameter model etna.models.BATSModel and etna.models.TBATSModel may result in different components set compared to the initialization parameters. In such case, a corresponding warning would be raised.

Methods

`fit`(ts)	Fit the transform and the decomposition model.
`fit_transform`(ts)	Fit and transform TSDataset.
`get_regressors_info`()	Return the list with regressors created by the transform.
`inverse_transform`(ts)	Inverse transform TSDataset.
`load`(path)	Load an object.
`params_to_tune`()	Get grid for tuning hyperparameters.
`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.
`transform`(ts)	Transform `TSDataset` inplace.

Attributes

This class stores its __init__ parameters as attributes.

fit(ts: TSDataset) → ModelDecomposeTransform[source]#

Fit the transform and the decomposition model.

Parameters:: ts (TSDataset) – dataset to fit the transform on.
Returns:: the fitted transform instance.
Return type:: ModelDecomposeTransform

fit_transform(ts: TSDataset) → TSDataset[source]#

Fit and transform TSDataset.

May be reimplemented. But it is not recommended.

Parameters:: ts (TSDataset) – TSDataset to transform.
Returns:: Transformed TSDataset.
Return type:: TSDataset

get_regressors_info() → List[str][source]#

Return the list with regressors created by the transform.

Return type:: List[str]

inverse_transform(ts: TSDataset) → TSDataset[source]#

Inverse transform TSDataset.

Do nothing.

Parameters:: ts (TSDataset) – TSDataset to be inverse transformed.
Returns:: TSDataset after applying inverse transformation.
Return type:: TSDataset

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 grid for tuning hyperparameters.

This is default implementation with empty grid.

Returns:: Empty grid.
Return type:: Dict[str, BaseDistribution]

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.

transform(ts: TSDataset) → TSDataset[source]#

Transform TSDataset inplace.

Parameters:: ts (TSDataset) – Dataset to transform.
Returns:: Transformed TSDataset.
Return type:: TSDataset