Annotation

The aeon.annotation module contains algorithms and tools for time series annotation. This no longer includes anomaly/outlier detection and time series segmentation, which are now in their own module.

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.

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

Implements a simple HMM fitted with Viterbi algorithm.

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

Hidden Markov Model with Poisson emissions.

Adapters

PyODAnnotator(estimator[, fmt, labels])

Transformer that applies outlier detector from pyOD.

Data Generation

Synthetic data generating functions.