etna.transforms.decomposition.StatisticsPerIntervalModel#

class StatisticsPerIntervalModel(statistics_function: Callable[[ndarray], float])[source]#

Bases: PerIntervalModel

StatisticsPerIntervalModel gets statistics from series and use them for prediction.

Init StatisticsPerIntervalModel.

Parameters:: statistics_function (Callable[[ndarray], float]) – function to compute statistics from series

Methods

`fit`(features, target, args, *kwargs)	Fit statistics from given target.
`predict`(features, args, *kwargs)	Build prediction from precomputed statistics.
`set_params`(**params)	Return new object instance with modified parameters.
`to_dict`()	Collect all information about etna object in dict.

Attributes

This class stores its __init__ parameters as attributes.

fit(features: ndarray, target: ndarray, *args, **kwargs) → StatisticsPerIntervalModel[source]#

Fit statistics from given target.

Parameters:

features (ndarray) – features of the series, will be ignored
target (ndarray) – target to compute statistics for

Returns:

fitted StatisticsPerIntervalModel

Return type:

self

predict(features: ndarray, *args, **kwargs) → ndarray[source]#

Build prediction from precomputed statistics.

Parameters:: features (ndarray) – features to build prediction for
Returns:: array of features len filled with statistics value
Return type:: prediction

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.