deseasonal¶

Classes

`DeseasonalModel`(value)	Enum for different types of deseasonality model.
`DeseasonalityTransform`(in_column, period[, ...])	Transform that uses `statsmodels.tsa.seasonal.seasonal_decompose()` to subtract seasonal component from the data.
`_OneSegmentDeseasonalityTransform`(in_column, ...)	Init _OneSegmentDeseasonalityTransform.

class DeseasonalModel(value)[source]¶: Enum for different types of deseasonality model.

class DeseasonalityTransform(in_column: str, period: int, model: Literal['additive', 'multiplicative'] = 'additive')[source]¶

Transform that uses statsmodels.tsa.seasonal.seasonal_decompose() to subtract seasonal component from the data.

Warning

This transform can suffer from look-ahead bias. For transforming data at some timestamp it uses information from the whole train part.

Init DeseasonalityTransform.

Parameters

in_column (str) – name of processed column
period (int) – size of seasonality
model (Literal['additive', 'multiplicative']) – ‘additive’ (Y[t] = T[t] + S[t] + e[t], default option) or ‘multiplicative’ (Y[t] = T[t] * S[t] * e[t])

fit(ts: etna.datasets.tsdataset.TSDataset) → etna.transforms.base.Transform¶

Fit the transform.

Parameters: ts (etna.datasets.tsdataset.TSDataset) – Dataset to fit the transform on.
Returns: The fitted transform instance.
Return type: etna.transforms.base.Transform

fit_transform(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset¶

Fit and transform TSDataset.

May be reimplemented. But it is not recommended.

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

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

Return the list with regressors created by the transform.

Return type: List[str]

inverse_transform(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset¶

Inverse transform TSDataset.

Apply the _inverse_transform method.

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

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][source]¶

Get default grid for tuning hyperparameters.

This grid tunes parameters: model. Other parameters are expected to be set by the user.

Returns: Grid to tune.
Return type: Dict[str, etna.distributions.distributions.BaseDistribution]

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.

transform(ts: etna.datasets.tsdataset.TSDataset) → etna.datasets.tsdataset.TSDataset¶

Transform TSDataset inplace.

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