Time Series Preprocessing¶
This module is for preprocessing time series data.

class
seglearn.preprocessing.
TargetRunLengthEncoder
(min_length=200)[source]¶ Takes a data set with a categorical target variable encoded as a time series and transforms it with run length encoding (RLE) of the target variable
RLE finds contiguous runs of the same target value within the input data and derives the transformed data set from the amalgum of all contiguous runs of all target classes from all series in the input data.
This is useful for generating “pure” series with no mixing of target variables from datasets that encode the target variable as a series (e.g. MHEALTH and PAMAP2)
Note that
seglearn
can handle datasets with target variables encoded as a series natively (usingSegmentXY
) and so this preprocessing is not required but may be helpful for some tasks. Effectively it will let you useSegmentX
on datasets that would otherwise requireSegmentXY
. Parameters
 min_lengthinteger > 1
minimum number of samples in a run for it to be included in the transformed data
Methods
fit
(self, X[, y])Fit the transform
fit_transform
(self, X, y[, sample_weight])Fit the data and transform (required by sklearn API)
get_params
(self[, deep])Get parameters for this estimator.
set_params
(self, \*\*params)Set the parameters of this estimator.
transform
(self, X, y[, sample_weight])Transforms the time series data with run length encoding of the target variable Note this transformation changes the number of samples in the data If sample_weight is provided, it is transformed to align to the new target encoding

fit
(self, X, y=None)[source]¶ Fit the transform
 Parameters
 Xarraylike, shape [n_series, …]
Time series data and (optionally) contextual data
 yNone
There is no need of a target in a transformer, yet the pipeline API requires this parameter.
 Returns
 selfobject
Returns self.

transform
(self, X, y, sample_weight=None)[source]¶ Transforms the time series data with run length encoding of the target variable Note this transformation changes the number of samples in the data If sample_weight is provided, it is transformed to align to the new target encoding
 Parameters
 Xarraylike, shape [n_series, …]
Time series data and (optionally) contextual data
 yarraylike shape [n_series, …]
target variable encoded as a time series
 sample_weightarraylike shape [n_series], default = None
sample weights
 Returns
 Xtarraylike, shape [n_rle_series, ]
transformed time series data
 ytarraylike, shape [n_rle_series]
target values for each series
 sample_weight_newarraylike shape [n_rle_series]
sample weights