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Time Series Segmentation with Shifting Means Hidden Markov Models : Volume 13, Issue 3 (01/08/2006)

By Kehagias, Ath.

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Book Id: WPLBN0004019749
Format Type: PDF Article :
File Size: Pages 14
Reproduction Date: 2015

Title: Time Series Segmentation with Shifting Means Hidden Markov Models : Volume 13, Issue 3 (01/08/2006)  
Author: Kehagias, Ath.
Volume: Vol. 13, Issue 3
Language: English
Subject: Science, Nonlinear, Processes
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Publication Date:
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications


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Fortin, V., & Kehagias, A. (2006). Time Series Segmentation with Shifting Means Hidden Markov Models : Volume 13, Issue 3 (01/08/2006). Retrieved from

Description: Aristotle University of Thessaloniki, School of Engineering, GR 541 24 Thessaloniki, Greece. We present a new family of hidden Markov models and apply these to the segmentation of hydrological and environmental time series. The proposed hidden Markov models have a discrete state space and their structure is inspired from the shifting means models introduced by Chernoff and Zacks and by Salas and Boes. An estimation method inspired from the EM algorithm is proposed, and we show that it can accurately identify multiple change-points in a time series. We also show that the solution obtained using this algorithm can serve as a starting point for a Monte-Carlo Markov chain Bayesian estimation method, thus reducing the computing time needed for the Markov chain to converge to a stationary distribution.

Time series segmentation with shifting means hidden markov models

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