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SupervisedIntervals

API referenceSource
Multivariate

Supervised interval feature transformer.

Quickstart

python
from sktime.transformations.supervised_intervals import SupervisedIntervals

estimator = SupervisedIntervals(n_intervals=50, min_interval_length=3, features=None, randomised_split_point=True, random_state=None, n_jobs=1, parallel_backend=None)

Parameters(7)

n_intervalsint, default=50
The number of times the supervised interval selection process is run. Each supervised extraction will output a varying amount of features based on series length, number of dimensions and the number of features.
min_interval_lengthint, default=3
The minimum length of extracted intervals. Minimum value of 3.
featuresfunction with a single 2d array-like parameter or list of said functions,

default=None

Functions used to extract features from selected intervals. If None, defaults to the following statistics used in [2]: [mean, median, std, slope, min, max, iqr, count_mean_crossing, count_above_mean].

randomised_split_pointbool, default=True
If True, the split point for interval extraction is randomised as is done in [2] rather than split in half.
n_jobsint, default=1

The number of jobs to run in parallel for both fit and transform. -1 means using all processors.

random_stateint or None, default=None
Seed for random number generation.
parallel_backendstr, ParallelBackendBase instance or None, default=None
Specify the parallelisation backend implementation in joblib, if None a ‘prefer’ value of “threads” is used by default. Valid options are “loky”, “multiprocessing”, “threading” or a custom backend. See the joblib Parallel documentation for more details.

References

  1. [1 ] Cabello, N., Naghizade, E., Qi, J. and Kulik, L., 2020, November. Fast and accurate time series classification through supervised interval search. In 2020 IEEE International Conference on Data Mining (ICDM) (pp. 948-953). IEEE. [2 ] Cabello, N., Naghizade, E., Qi, J. and Kulik, L., 2021. Fast, accurate and interpretable time series classification through randomization. arXiv preprint arXiv:2105.14876.