GreedyGaussianSegmentation

Greedy Gaussian Segmentation Estimator.

Quickstart

from sktime.detection.ggs import GreedyGaussianSegmentation

estimator = GreedyGaussianSegmentation(k_max: int=10, lamb: float=1.0, max_shuffles: int=250, verbose: bool=False, random_state: int=None)

Parameters(5)

k_maxint, default=10

Maximum number of change points to find. The number of segments is thus k+1.

lambfloat, default=1.0

Regularization parameter lambda (>= 0), which controls the amount of (inverse) covariance regularization. A higher lambda favors simpler models.

max_shufflesint, default=250

Maximum number of shuffles

verbosebool, default=False

If True, verbose output is enabled.

random_stateint or np.random.RandomState, default=None

Either random seed or an instance of np.random.RandomState

References

1. [1 ] Hallac, D., Nystrup, P. & Boyd, S., “Greedy Gaussian segmentation of multivariate time series.”, Adv Data Anal Classif 13, 727-751 (2019). https://doi.org/10.1007/s11634-018-0335-0