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

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 fin...