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

Faicel Chamroukhi (1, 2) , Allou Samé (2) , Gérard Govaert (1) , Patrice Aknin (2)

1 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]
2 INRETS/LTN - Laboratoire des Technologies Nouvelles

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.

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Faicel Chamroukhi, Allou Samé, Gérard Govaert, Patrice Aknin. A regression model with a hidden logistic process for feature extraction from time serie. IJCNN'09 International Joint Conference on Neural Networks, Jun 2009, Atlanta, United States. pp.1-8. ⟨hal-00447844⟩