Title :
A regression model with a hidden logistic process for feature extraction from time series
Author :
Chamroukhi, Faicel ; Samé, Allou ; Govaert, Gérard ; Aknin, Patrice
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.
Keywords :
expectation-maximisation algorithm; feature extraction; least squares approximations; logistics; maximum likelihood sequence estimation; regression analysis; time series; discrete hidden logistic process; expectation maximization algorithm; feature extraction; maximum likelihood method; multiclass iterative reweighted least-squares; regression model; time series; Dynamic programming; Feature extraction; Hidden Markov models; Iterative algorithms; Iterative methods; Linear regression; Logistics; Parameter estimation; Rail transportation; Switches;
Conference_Titel :
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-3548-7
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2009.5178921