module timeseries.preprocessing

Inheritance diagram of mlinsights.timeseries.preprocessing

Short summary

module mlinsights.timeseries.preprocessing

Timeseries preprocessing.

source on GitHub

Classes

class

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TimeSeriesDifference

Computes timeseries differences.

TimeSeriesDifferenceInv

Computes the reverse of TimeSeriesDifference.

Properties

property

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_repr_html_

HTML representation of estimator. This is redundant with the logic of _repr_mimebundle_. The latter should …

_repr_html_

HTML representation of estimator. This is redundant with the logic of _repr_mimebundle_. The latter should …

degree

Returns the degree.

Methods

method

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__init__

__init__

fit

Stores the first values.

fit

Checks that estimator is fitted.

get_fct_inv

Returns the reverse tranform.

transform

Transforms both X and y. Returns X and y, returns sample_weight as well if not None.

transform

Transforms both X and y. Returns X and y, returns sample_weight as well if not None.

Documentation

Timeseries preprocessing.

source on GitHub

class mlinsights.timeseries.preprocessing.TimeSeriesDifference(degree=1)[source]

Bases: mlinsights.timeseries.base.BaseReciprocalTimeSeriesTransformer

Computes timeseries differences.

source on GitHub

Parameters

degree – number of differences

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__init__(degree=1)[source]
Parameters

degree – number of differences

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property degree

Returns the degree.

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fit(X, y, sample_weight=None)[source]

Stores the first values.

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get_fct_inv()[source]

Returns the reverse tranform.

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transform(X, y, sample_weight=None)[source]

Transforms both X and y. Returns X and y, returns sample_weight as well if not None.

source on GitHub

class mlinsights.timeseries.preprocessing.TimeSeriesDifferenceInv(estimator)[source]

Bases: mlinsights.timeseries.base.BaseReciprocalTimeSeriesTransformer

Computes the reverse of TimeSeriesDifference.

source on GitHub

Parameters

estimator – of type TimeSeriesDifference

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__init__(estimator)[source]
Parameters

estimator – of type TimeSeriesDifference

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fit(X=None, y=None, sample_weight=None)[source]

Checks that estimator is fitted.

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transform(X, y, sample_weight=None)[source]

Transforms both X and y. Returns X and y, returns sample_weight as well if not None.

source on GitHub