module `timeseries.base`#

Short summary#

module mlinsights.timeseries.base

Base class for timeseries.

Classes#

class	truncated documentation
`BaseReciprocalTimeSeriesTransformer`	Base for all timeseries preprocessing automatically applied within a predictor.
`BaseTimeSeries`	Base class to build a predictor on timeseries. The class computes one or several predictions at each time, between …
`TimeSeriesRegressorMixin`	Addition to sklearn.base.RegressorMixin.

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 …

Methods#

method	truncated documentation
`__init__`
`__init__`
`_applies_preprocessing`	Applies the preprocessing to the series.
`_applies_preprocessing_inv`	Applies the preprocessing to the series.
`_base_fit_predict`	Trains the preprocessing and returns the modified X, y, sample_weight.
`_fit_preprocessing`	Applies the preprocessing. X, y, sample_weight.
`fit`	Stores the first values.
`get_fct_inv`	Returns the reverse tranform.
`has_preprocessing`	Tells if there is one preprocessing.
`score`	Scores the prediction using `ts_mape()`
`transform`	Transforms both X and y. Returns X and y, returns sample_weight as well if not None. The …

Documentation#

Base class for timeseries.

source on GitHub

class mlinsights.timeseries.base.BaseReciprocalTimeSeriesTransformer(context_length=0)#

Bases: BaseReciprocalTransformer

Base for all timeseries preprocessing automatically applied within a predictor.

source on GitHub

Parameters:: context_length – number of previous observations to build or rebuild the observations

source on GitHub

__init__(context_length=0)#

Parameters:: context_length – number of previous observations to build or rebuild the observations

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, context=None)#

Transforms both X and y. Returns X and y, returns sample_weight as well if not None. The context is used when the y series stored in the predictor is not related to the y series given to the transform method.

source on GitHub

class mlinsights.timeseries.base.BaseTimeSeries(past=1, delay1=1, delay2=2, use_all_past=False, preprocessing=None)#

Bases: BaseEstimator

Base class to build a predictor on timeseries. The class computes one or several predictions at each time, between delay1 and delay2. It computes: $\hat{Y_{t+d} = f(Y_{t-1}, ..., Y_{t-p})}$ with d in [delay1, delay2[ and $1 \leqslant p \leqslant past$ .

source on GitHub

Parameters:

past – values to use to predict
delay1 – the model computes the first prediction for time=t + delay1
delay2 – the model computes the last prediction for time=t + delay2 excluded
use_all_past – use all past features, not only the timeseries
preprocessing – preprocessing to apply before predicting, only the timeseries itselves, it can be a difference, it must be of type BaseReciprocalTimeSeriesTransformer

source on GitHub

__init__(past=1, delay1=1, delay2=2, use_all_past=False, preprocessing=None)#

Parameters:

past – values to use to predict
delay1 – the model computes the first prediction for time=t + delay1
delay2 – the model computes the last prediction for time=t + delay2 excluded
use_all_past – use all past features, not only the timeseries
preprocessing – preprocessing to apply before predicting, only the timeseries itselves, it can be a difference, it must be of type BaseReciprocalTimeSeriesTransformer