_OneSegmentSTLTransform¶

class _OneSegmentSTLTransform(in_column: str, period: int, model: Union[str, statsmodels.tsa.base.tsa_model.TimeSeriesModel] = 'arima', robust: bool = False, model_kwargs: Optional[Dict[str, Any]] = None, stl_kwargs: Optional[Dict[str, Any]] = None)[source]¶

Bases: etna.transforms.base.OneSegmentTransform

Init _OneSegmentSTLTransform.

Parameters

in_column (str) – name of processed column
period (int) – size of seasonality
model (Union[str, statsmodels.tsa.base.tsa_model.TimeSeriesModel]) –
model to predict trend, default options are:
1. ”arima”: ARIMA(data, 1, 1, 0) (default)
2. ”holt”: ETSModel(data, trend='add')
Custom model should be a subclass of statsmodels.tsa.base.tsa_model.TimeSeriesModel and have method get_prediction (not just predict)
robust (bool) – flag indicating whether to use robust version of STL
model_kwargs (Optional[Dict[str, Any]]) – parameters for the model like in statsmodels.tsa.seasonal.STLForecast
stl_kwargs (Optional[Dict[str, Any]]) – additional parameters for statsmodels.tsa.seasonal.STLForecast

Inherited-members

Methods

`fit`(df)	Perform STL decomposition and fit trend model.
`fit_transform`(df)	Fit and transform Dataframe.
`inverse_transform`(df)	Add trend and seasonal component.
`set_params`(**params)	Return new object instance with modified parameters.
`to_dict`()	Collect all information about etna object in dict.
`transform`(df)	Subtract trend and seasonal component.

fit(df: pandas.core.frame.DataFrame) → etna.transforms.decomposition.stl._OneSegmentSTLTransform[source]¶

Perform STL decomposition and fit trend model.

Parameters: df (pandas.core.frame.DataFrame) – Features dataframe with time
Returns: result – instance after processing
Return type: _OneSegmentSTLTransform

fit_transform(df: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame¶

Fit and transform Dataframe.

May be reimplemented. But it is not recommended.

Parameters: df (pandas.core.frame.DataFrame) – Dataframe in etna long format to transform.
Returns: Transformed Dataframe.
Return type: pandas.core.frame.DataFrame

inverse_transform(df: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source]¶

Add trend and seasonal component.

Parameters: df (pandas.core.frame.DataFrame) – Features dataframe with time
Returns: result – Dataframe with extracted features
Return type: pd.DataFrame

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(df: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame[source]¶

Subtract trend and seasonal component.

Parameters: df (pandas.core.frame.DataFrame) – Features dataframe with time
Returns: result – Dataframe with extracted features
Return type: pd.DataFrame