A fuzzy/Bayesian approach for the time series change point detection problem

Pesquisa Operacional

Marcos Flávio S.v. D'angelo²  Reinaldo M. Palhares¹  Ricardo H.c. Takahashi¹  Rosangela H. Loschi¹ 

[1] ,Universidade Estadual de Montes Claros Departamento de Ciência da Computação Montes Claros MG ,Brazil

关键词: change point;    fuzzy clustering;    Metropolis-Hastings;   

DOI  :  10.1590/S0101-74382011000200002

来源: SciELO

【 摘 要 】

This paper addresses the change point detection problem in time series. A methodology based on the Metropolis-Hastings algorithm applied to time series modeled as a process with Beta distribution is discussed. In order to make this methodology useful in practice, a fuzzy cluster technique is applied to the initial time series at first, generating a new data set with Beta distribution. Bayesian procedures are considered for inference and the Metropolis-Hastings algorithm is used to sample from the posteriors. In the clustering process, a Kohonen neural network is used having as objective to find the best centers of the time series to be used in the fuzzyfication process. Finally, it will be presented a simulation results in the series of the electric energy consumption in Brazil, between January of 1976 and December of 2000, five months before the blackout occurred in 2001. Such result illustrates the efficiency of the proposed methodology for change point detection in time series.

【 授权许可】

CC BY   
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