statistics_based

Classes

MeanPerIntervalModel()

MeanPerIntervalModel.

MedianPerIntervalModel()

MedianPerIntervalModel.

StatisticsPerIntervalModel(statistics_function)

StatisticsPerIntervalModel gets statistics from series and use them for prediction.
class MeanPerIntervalModel[source]

MeanPerIntervalModel.

MeanPerIntervalModel is a shortcut for :py:class:`etna.transforms.decomposition.change_points_based.per_interval_models.statistics_based.StatisticsPerIntervalModel that uses mean value as statistics function.

Init StatisticsPerIntervalModel.

Parameters

statistics_function – function to compute statistics from series

fit(features: numpy.ndarray, target: numpy.ndarray, *args, **kwargs) → etna.transforms.decomposition.change_points_based.per_interval_models.statistics_based.StatisticsPerIntervalModel

Fit statistics from given target.

Parameters

  • features (numpy.ndarray) – features of the series, will be ignored

  • target (numpy.ndarray) – target to compute statistics for

Returns

fitted StatisticsPerIntervalModel

Return type

self

predict(features: numpy.ndarray, *args, **kwargs) → numpy.ndarray

Build prediction from precomputed statistics.

Parameters

features (numpy.ndarray) – features to build prediction for

Returns

array of features len filled with statistics value

Return type

prediction

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.

class MedianPerIntervalModel[source]

MedianPerIntervalModel.

MedianPerIntervalModel is a shortcut for :py:class:`etna.transforms.decomposition.change_points_based.per_interval_models.statistics_based.StatisticsPerIntervalModel that uses median value as statistics function.

Init StatisticsPerIntervalModel.

Parameters

statistics_function – function to compute statistics from series

fit(features: numpy.ndarray, target: numpy.ndarray, *args, **kwargs) → etna.transforms.decomposition.change_points_based.per_interval_models.statistics_based.StatisticsPerIntervalModel

Fit statistics from given target.

Parameters

  • features (numpy.ndarray) – features of the series, will be ignored

  • target (numpy.ndarray) – target to compute statistics for

Returns

fitted StatisticsPerIntervalModel

Return type

self

predict(features: numpy.ndarray, *args, **kwargs) → numpy.ndarray

Build prediction from precomputed statistics.

Parameters

features (numpy.ndarray) – features to build prediction for

Returns

array of features len filled with statistics value

Return type

prediction

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.

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

StatisticsPerIntervalModel gets statistics from series and use them for prediction.

Init StatisticsPerIntervalModel.

Parameters

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

fit(features: numpy.ndarray, target: numpy.ndarray, *args, **kwargs) → etna.transforms.decomposition.change_points_based.per_interval_models.statistics_based.StatisticsPerIntervalModel[source]

Fit statistics from given target.

Parameters

  • features (numpy.ndarray) – features of the series, will be ignored

  • target (numpy.ndarray) – target to compute statistics for

Returns

fitted StatisticsPerIntervalModel

Return type

self

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

Build prediction from precomputed statistics.

Parameters

features (numpy.ndarray) – features to build prediction for

Returns

array of features len filled with statistics value

Return type

prediction

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.