SignatureClassifier

Classification module using signature-based features.

Quickstart

from sktime.classification.feature_based import SignatureClassifier

estimator = SignatureClassifier(estimator=None, augmentation_list=('basepoint', 'addtime'), window_name='dyadic', window_depth=3, window_length=None, window_step=None, rescaling=None, sig_tfm='signature', depth=4, random_state=None)

Parameters(10)

estimatorsklearn estimator, default=RandomForestClassifier

This should be any sklearn-type estimator. Defaults to RandomForestClassifier.

augmentation_list: list of tuple of strings, default=(“basepoint”, “addtime”)

List of augmentations to be applied before the signature transform is applied.

window_name: str, default=”dyadic”

The name of the window transform to apply.

window_depth: int, default=3

The depth of the dyadic window. (Active only if window_name == 'dyadic'].

window_length: int, default=None

The length of the sliding/expanding window. (Active only if `window_name in [‘sliding, ‘expanding’].

window_step: int, default=None

The step of the sliding/expanding window. (Active only if `window_name in [‘sliding, ‘expanding’].

rescaling: str, default=None

The method of signature rescaling.

sig_tfm: str, default=”signature”

String to specify the type of signature transform. One of: [‘signature’, ‘logsignature’]).

depth: int, default=4

Signature truncation depth.

random_state: int, default=None

Random state initialisation.

References

1. [1 ] Morrill, James, et al. “A generalised signature method for multivariate time series feature extraction.” arXiv preprint arXiv:2006.00873 (2020). https://arxiv.org/pdf/2006.00873.pdf