Transformer

SupervisedIntervals

Supervised interval feature transformer.

Extracts intervals in fit using the supervised process described in [1]. Interval sub-series are extracted for each input feature, and the usefulness of that feature extracted on an interval is evaluated using the Fisher score metric. Intervals are continually split in half, with the better scoring half retained as a feature for the transform.

Multivariate capability is added by running the supervised interval extraction process on each dimension of the input data.

As the extracted interval features are already extracted for the supervised evaluation in fit, the fit_transform method is recommended if the transformed fit data is required.

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]

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.