Time series segmentation with shifting means hidden markov models

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 Cher-noff and Zacks and by Salas and Boes. An estimation method inspired from the EM… (More)

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