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

Neural Networks

Volumn 22, Issue 5-6, 2009, Pages 593-602

  Chamroukhi, Faicel ^a,b   Samé, Allou ^a   Govaert, Gérard ^b   Aknin, Patrice ^a  

^a Laboratory of New Technologies   (France)

^b UNIVERSITÉ DE TECHNOLOGIE DE COMPIÈGNE   (France)

Author keywords

Classification; EM algorithm; Hidden process; Maximum likelihood; Regression; Time series

Indexed keywords

ALTERNATIVE APPROACH; BAUM-WELCH ALGORITHMS; CLASSIFICATION; EM ALGORITHM; EM ALGORITHMS; EXPECTATION-MAXIMIZATION ALGORITHMS; EXPERIMENTAL STUDIES; GLOBAL OPTIMIZATION ALGORITHM; HIDDEN PROCESS; ITERATIVE REWEIGHTED LEAST SQUARE; LOGISTIC PROCESS; MAXIMUM LIKELIHOOD METHODS; MODEL PARAMETERS; MODELING TECHNIQUE; MULTI-CLASS; NEW APPROACHES; ON DYNAMICS; PIECEWISE REGRESSION; POLYNOMIAL REGRESSION MODELS; PROCESS PARAMETERS; RAILWAY INFRASTRUCTURE; REAL WORLD DATA; REGRESSION; REGRESSION MODEL; REMOTE MONITORING; SIMULATED DATA; SWITCH MECHANISM; SWITCH OPERATION; SYNTHETIC REPRESENTATIONS; TIME SERIES MODELING;

ALGORITHMS; BIOINFORMATICS; GLOBAL OPTIMIZATION; MAXIMUM LIKELIHOOD; PARAMETER ESTIMATION; RAILROAD TRANSPORTATION; SYSTEMS ENGINEERING; TIME SERIES;

REGRESSION ANALYSIS;

ALGORITHM; ANALYTICAL PARAMETERS; ARTICLE; CLASSIFICATION; DATA ANALYSIS; EXPERIMENTAL STUDY; HIDDEN MARKOV MODEL; MAXIMUM LIKELIHOOD METHOD; PRIORITY JOURNAL; PROCESS OPTIMIZATION; RAILWAY; REGRESSION ANALYSIS; STATISTICAL MODEL; TIME SERIES ANALYSIS;

ALGORITHMS; COMPUTER SIMULATION; LIKELIHOOD FUNCTIONS; LOGISTIC MODELS; MARKOV CHAINS; MODELS, THEORETICAL; RAILROADS; REGRESSION ANALYSIS; TIME;

EID: 68149170833     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2009.06.040     Document Type: Article

References (23)

1. Baum L., Petrie T., Soules G., and Weiss N. A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Annals of Mathematical Statistics 41 (1970) 164-171
2. Bellman R. On the approximation of curves by line segments using dynamic programming. Communications of the Association for Computing Machinery (CACM) 4 6 (1961) 284
3. Brailovsky V.L., and Kempner Y. Application of piecewise regression to detecting internal structure of signal. Pattern Recognition 25 11 (1992) 1361-1370
4. Chamroukhi, F., Samé, A., Govaert, G., & Aknin, P. (2009). A regression model with a hidden logistic process for feature extraction from time series. In: International joint conference on neural networks
5. Chen K., Xu L., and Chi H. Improved learning algorithms for mixture of experts in multiclass classification. Neural Networks 12 9 (1999) 1229-1252
6. Dempster A.P., Laird N.M., and Rubin D.B. Maximum likelihood from incomplete data via the em algorithm. Journal of The Royal Statistical Society, B 39 1 (1977) 1-38
7. Ferrari-Trecate, G., & Muselli, M. (2002). A new learning method for piecewise linear regression. In: International conference on artificial neural networks (pp. 28-30)
8. Ferrari-Trecate G., Muselli M., Liberati D., and Morari M. A clustering technique for the identification of piecewise affine and hybrid systems. Automatica 39 (2002) 205-217
9. Fisher W.D. On grouping for maximum homogeneity. Journal of the American Statistical Association 53 (1958) 789-798
10. Fridman, M. (1993). Hidden markov model regression. Technical report. Institute of Mathematics, University of Minnesota
11. Green P. Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives. Journal of The Royal Statistical Society, B 46 2 (1984) 149-192
12. Hastie T., and Tibshirani R. Discriminant analysis by gaussian mixtures. Journal of the Royal Statistical Society, B 58 (1996) 155-176
13. Jordan M.I., and Jacobs R.A. Hierarchical mixtures of experts and the em algorithm. Neural Computation 6 (1994) 181-214
14. Krishnapuram B., Carin L., Figueiredo M., and Hartemink A. Sparse multinomial logistic regression: Fast algorithms and generalization bounds. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 6 (2005) 957-968
15. Lechevalier, Y. (1990). Optimal clustering on ordered set. Technical report. The French National Institute for Research in Computer Science and Control (INRIA)
16. McGee V.E., and Carleton W.T. Piecewise regression. Journal of the American Statistical Association 65 (1970) 1109-1124
17. McLachlan G.J., and Krishnan T. The EM algorithm and extensions (1997), New York: Wiley
18. McLachlan G.J., and Peel D. Finite mixture models (2000), New York: Wiley
19. Quandt R.E., and Ramsey J.B. Estimating mixtures of normal distributions and switching regressions. Journal of the American Statistical Association 73 (1978) 730-738
20. Rabiner L.R. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77 2 (1989) 257-286
21. Samé, A., Aknin, P., & Govaert, G. (2007). Classification automatique pour la segmentation des signaux unidimensionnels. Rencontres de la SFC, ENST, Paris
22. Schwarz G. Estimating the dimension of a model. Annals of Statistics 6 (1978) 461-464
23. Waterhouse, S.R. (1997). Classification and regression using mixtures of experts. Ph.D. thesis. Department of Engineering, Cambridge University