module `timeseries.preprocessing`#

Short summary#

module mlinsights.timeseries.preprocessing

Timeseries preprocessing.

source on GitHub

Classes#

class	truncated documentation
`TimeSeriesDifference`	Computes timeseries differences.
`TimeSeriesDifferenceInv`	Computes the reverse of `TimeSeriesDifference`.

Properties#

property	truncated documentation
`_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	truncated documentation
`__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)#

Bases: BaseReciprocalTimeSeriesTransformer

Computes timeseries differences.

source on GitHub

Parameters:: degree – number of differences

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__init__(degree=1)#

Parameters:: degree – number of differences

source on GitHub

property degree#

Returns the degree.

source on GitHub

fit(X, y, sample_weight=None)#

Stores the first values.

source on GitHub

get_fct_inv()#

Returns the reverse tranform.

source on GitHub

transform(X, y, sample_weight=None)#

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)#

Bases: BaseReciprocalTimeSeriesTransformer

Computes the reverse of TimeSeriesDifference.

source on GitHub

Parameters:: estimator – of type TimeSeriesDifference

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__init__(estimator)#

Parameters:: estimator – of type TimeSeriesDifference

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fit(X=None, y=None, sample_weight=None)#

Checks that estimator is fitted.

source on GitHub

transform(X, y, sample_weight=None)#

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

source on GitHub