  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.
