Document Detail

Time series modeling by a regression approach based on a latent process.
MedLine Citation:
PMID:  19616918     Owner:  NLM     Status:  MEDLINE    
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.
Faicel Chamroukhi; Allou Samé; Gérard Govaert; Patrice Aknin
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Publication Detail:
Type:  Journal Article     Date:  2009-07-04
Journal Detail:
Title:  Neural networks : the official journal of the International Neural Network Society     Volume:  22     ISSN:  1879-2782     ISO Abbreviation:  Neural Netw     Publication Date:    2009 Jul-Aug
Date Detail:
Created Date:  2009-08-11     Completed Date:  2009-11-02     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8805018     Medline TA:  Neural Netw     Country:  United States    
Other Details:
Languages:  eng     Pagination:  593-602     Citation Subset:  IM    
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.
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MeSH Terms
Computer Simulation
Likelihood Functions
Logistic Models
Markov Chains
Models, Theoretical*
Regression Analysis*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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