Transformer

Rocket

RandOm Convolutional KErnel Transform (ROCKET).

Quickstart

python

from sktime.transformations.rocket import Rocket

estimator = Rocket(num_kernels=10000, normalise=True, n_jobs=1, random_state=None)

Parameters(4)

num_kernelsint, default=10,000: number of random convolutional kernels.
normaliseboolean, default True: whether or not to normalise the input time series per instance.
n_jobsint, default=1: The number of jobs to run in parallel for transform. -1 means use all processors.
random_stateNone or int, optional, default = None

Examples

>>> from sktime.transformations.rocket import Rocket
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test (split = "train")
>>> X_test, y_test = load_unit_test (split = "test")
>>> trf = Rocket (num_kernels = 512)
>>> trf. fit (X_train) Rocket(
... )
>>> X_train = trf. transform (X_train)
>>> X_test = trf. transform (X_test)

References

[1 ] Tan, Chang Wei and Dempster, Angus and Bergmeir, Christoph and Webb, Geoffrey I, “ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels”,2020, https://link.springer.com/article/10.1007/s10618-020-00701-z, https://arxiv.org/abs/1910.13051