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A new approach for feature extraction from time series is proposed in thispaper. This approach consists of a specific regression model incorporating adiscrete hidden logistic process. The model parameters are estimated by themaximum likelihood method performed by a dedicated Expectation Maximization(EM) algorithm. The parameters of the hidden logistic process, in the innerloop of the EM algorithm, are estimated using a multi-class IterativeReweighted Least-Squares (IRLS) algorithm. A piecewise regression algorithm andits iterative variant have also been considered for comparisons. Anexperimental study using simulated and real data reveals good performances ofthe proposed approach.

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