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Neural Netw. 2009 Jul-Aug;22(5-6):593-602. doi: 10.1016/j.neunet.2009.06.040. Epub 2009 Jul 4.

Time series modeling by a regression approach based on a latent process.

Author information

1
French National Institute for Transport and Safety Research (INRETS), Laboratory of New Technologies (LTN), 2 Rue de la Butte Verte, 93166 Noisy-le-Grand Cedex, France. faicel.chamroukhi@inrets.fr

Abstract

Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process parameters. To evaluate the proposed approach, an experimental study on simulated data and real world data was performed using two alternative approaches: a heteroskedastic piecewise regression model using a global optimization algorithm based on dynamic programming, and a Hidden Markov Regression Model whose parameters are estimated by the Baum-Welch algorithm. Finally, in the context of the remote monitoring of components of the French railway infrastructure, and more particularly the switch mechanism, the proposed approach has been applied to modeling and classifying time series representing the condition measurements acquired during switch operations.

PMID:
19616918
DOI:
10.1016/j.neunet.2009.06.040
[Indexed for MEDLINE]

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