A regression model with a hidden logistic process for feature extraction from time series

TL;DR

This paper introduces a novel regression model with a hidden logistic process for effective feature extraction from time series data, utilizing EM and IRLS algorithms for parameter estimation.

Contribution

It presents a new model combining regression with a hidden logistic process and develops specialized algorithms for parameter estimation, improving feature extraction from time series.

Findings

Good performance on simulated data

Effective on real-world data

Outperforms traditional methods

Abstract

A new approach for feature extraction from time series is proposed in this paper. This approach consists of a specific regression model incorporating a discrete hidden logistic process. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The parameters of the hidden logistic process, in the inner loop of the EM algorithm, are estimated using a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm. A piecewise regression algorithm and its iterative variant have also been considered for comparisons. An experimental study using simulated and real data reveals good performances of the proposed approach.