A state-space approach to modeling functional time series application to rail supervision. Samé, A. & El-Assaad, H. In *2014 22nd European Signal Processing Conference (EUSIPCO)*, pages 1402-1406, Sep., 2014.

Paper abstract bibtex

Paper abstract bibtex

This article introduces a state-space model for the dynamic modeling of curve sequences within the framework of railway switches online monitoring. In this context, each curve has the peculiarity of being subject to multiple changes in regime. The proposed model consists of a specific latent variable regression model whose coefficients are supposed to evolve dynamically in the course of time. Its parameters are recursively estimated across a sequence of curves through an online Expectation-Maximization (EM) algorithm. The experimental study conducted on two real power consumption curve sequences from the French high speed network has shown encouraging results.

@InProceedings{6952500, author = {A. Samé and H. El-Assaad}, booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)}, title = {A state-space approach to modeling functional time series application to rail supervision}, year = {2014}, pages = {1402-1406}, abstract = {This article introduces a state-space model for the dynamic modeling of curve sequences within the framework of railway switches online monitoring. In this context, each curve has the peculiarity of being subject to multiple changes in regime. The proposed model consists of a specific latent variable regression model whose coefficients are supposed to evolve dynamically in the course of time. Its parameters are recursively estimated across a sequence of curves through an online Expectation-Maximization (EM) algorithm. The experimental study conducted on two real power consumption curve sequences from the French high speed network has shown encouraging results.}, keywords = {condition monitoring;expectation-maximisation algorithm;power consumption;railways;recursive estimation;regression analysis;state-space methods;switches;time series;state-space model;curve sequence dynamic modeling;functional time series modeling application;rail supervision;railway switches online monitoring;specific latent variable regression model;recursive estimation;online expectation maximization algorithm;online EM algorithm;power consumption curve sequence;French high speed network;Power demand;Logistics;Mathematical model;Vectors;Monitoring;Rail transportation;Time series analysis;Time series of functional data;state-space model;Kalman filtering;online Expectation-Maximization (EM) algorithm;condition monitoring}, issn = {2076-1465}, month = {Sep.}, url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569919069.pdf}, }

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