tsseg.algorithms.hmm package

HMM — Hidden Markov Model state annotation via Viterbi decoding.

Description

Annotates a univariate time series with hidden-state labels using the Viterbi algorithm. The emission distributions, transition matrix and initial probabilities must be provided by the user (no EM learning). This makes the detector suitable as a baseline or when prior distributions are known.

Type: state detection
Supervision: requires known distributions (no learning)
Scope: univariate only

Parameters

Name	Type	Default	Description
`emission_funcs`	list / None	`None`	List of callables (PDFs) for each hidden state. Default: two-state Gaussian `N(0,1)` / `N(1,1)`.
`transition_prob_mat`	ndarray / None	`None`	Row-stochastic transition matrix. Default: `[[0.9,0.1],[0.1,0.9]]`.
`initial_probs`	ndarray / None	`None`	Initial state probabilities. Default: uniform.

Usage

from tsseg.algorithms import HMMDetector

detector = HMMDetector()   # default two-state Gaussian
labels = detector.fit_predict(X)

Implementation: Adapted from aeon. BSD 3-Clause.

Reference: Rabiner (1989), A tutorial on hidden Markov models and selected applications in speech recognition, Proceedings of the IEEE.

Submodules

tsseg.algorithms.hmm.detector module

Module contents