Articles | Volume 13, issue 3
Nonlin. Processes Geophys., 13, 339–352, 2006
https://doi.org/10.5194/npg-13-339-2006
Nonlin. Processes Geophys., 13, 339–352, 2006
https://doi.org/10.5194/npg-13-339-2006

  01 Aug 2006

01 Aug 2006

Time series segmentation with shifting means hidden markov models

Ath. Kehagias1 and V. Fortin2 Ath. Kehagias and V. Fortin
  • 1Aristotle University of Thessaloniki, School of Engineering, GR 541 24 Thessaloniki, Greece
  • 2Numerical Weather Prediction Research, Meteorological Research Division, Environment Canada, Dorval, Quebec H9P 1J3, Canada

Abstract. 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.