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

AUTOR(ES)

D'Angelo, Marcos Flávio S.V., Palhares, Reinaldo M., Takahashi, Ricardo H.C., H. Loschi, Rosangela

FONTE

Pesquisa Operacional

DATA DE PUBLICAÇÃO

2011-08

RESUMO

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.

Documentos Relacionados

• MONITORING THE STATOR CURRENT IN INDUCTION MACHINES FOR POSSIBLE FAULT DETECTION: A FUZZY/BAYESIAN APPROACH FOR THE PROBLEM OF TIME SERIES MULTIPLE CHANGE POINT DETECTION
• Comparison between the complete Bayesian method and empirical Bayesian method for ARCH models using Brazilian financial time series
• Comparison between methods of tonometry: time for a change of approach.
• Time for a Change
• A geometrical approach for fuzzy DEA frontiers.