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论文类型：期刊论文

发表时间：2016-06-01

发表刊物：ICIC Express Letters

收录刊物：EI

卷号：10

期号：6

页面范围：1427-1433

ISSN号：1881803X

摘要：In this paper, an approach based on hidden Markov models (HMM) is applied to segmenting multivariate time series. In this algorithm, each observation in the underlying time series is dependent on a corresponding hidden state, and the segmentation problem comes down to finding the hidden states. When related parameters are given, the states can be obtained under a maximum likelihood framework using the Viterbi algorithm. Some meaningful methods including vector autoregression (VAR) models are utilized to estimate the parameters. In this way, the state estimation step and the parameter estimation step will perform repeatedly until the convergence condition is satisfied. The segmentation procedure is evaluated by a hydrometeorological time series. © 2016 ISSN.