Back to models
Transformer

RandomIntervalSegmenter

API referenceSource
Unequal-length seriesEqualizes series length

Random interval segmenter transformer.

Transformer that segments time-series into random intervals with random starting points and lengths. Some intervals may overlap and may be duplicates.

Quickstart

python
from sktime.transformations.segment import RandomIntervalSegmenter

estimator = RandomIntervalSegmenter(n_intervals='sqrt', min_length=None, max_length=None, random_state=None)

Tags

Capabilities

  • Unequal-length series
  • Equalizes series length: Supported
  • Multivariate: Not supported
  • Inverse transform: Not supported
  • Missing values: Not supported
  • Removes missing values: Not supported

Properties

Input typescitype:transform-input
Series
Output typescitype:transform-output
Series
Label typescitype:transform-labels
None
Fit is emptyfit_is_empty
No
Keeps the time indextransform-returns-same-time-index
No
Requires Xrequires_X
Yes
Requires yrequires_y
No
X and y need the same indexX-y-must-have-same-index
No

Parameters(2)

n_intervalsstr, int or float

Number of intervals to generate. - If “log”, log of m is used where m is length of time series. - If “sqrt”, sqrt of m is used. - If “random”, random number of intervals is generated. - If int, n_intervals intervals are generated. - If float, int(n_intervals * m) is used with n_intervals giving the fraction of intervals of the time series length.

For all arguments relative to the length of the time series, the generated number of intervals is always at least 1.

Default is “sqrt”.

random_stateint, RandomState instance or None, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.