A regression model with a hidden logistic process for feature extraction from time series
This is an incremental improvement for time series analysis, potentially benefiting fields like signal processing or finance.
The authors tackled feature extraction from time series by proposing a regression model with a hidden logistic process, estimated via a dedicated EM algorithm, and reported good performances in experiments with simulated and real data.
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.