Time series annotation

The sktime.annotation module contains algorithms and tools for time series annotation tasks, like anomaly/outlier detection, and time series segmentation.

Time Series Segmentation

ClaSPSegmentation([period_length, n_cps, ...])

ClaSP (Classification Score Profile) Segmentation.

EAgglo([member, alpha, penalty])

Hierarchical agglomerative estimation of multiple change points.

GaussianHMM([n_components, covariance_type, ...])

Hidden Markov Model with Gaussian emissions.

GMMHMM([n_components, n_mix, min_covar, ...])

Hidden Markov Model with Gaussian mixture emissions.

GreedyGaussianSegmentation([k_max, lamb, ...])

Greedy Gaussian Segmentation Estimator.

HMM(emission_funcs, transition_prob_mat[, ...])

Implements a simple HMM fitted with Viterbi algorithm.

InformationGainSegmentation([k_max, step])

Information Gain based Temporal Segmentation (IGTS) Estimator.

PoissonHMM([n_components, startprob_prior, ...])

Hidden Markov Model with Poisson emissions.

STRAY([alpha, k, knn_algorithm, p, ...])

STRAY: robust anomaly detection in data streams with concept drift.

Adapters

PyODAnnotator(estimator[, fmt, labels])

Transformer that applies outlier detector from pyOD.

Data Generation

Synthetic data generating functions.