10.4225/03/5934f8a05b1c7 Oliver, Jonathan J. Jonathan J. Oliver Forbes, Catherine S. Catherine S. Forbes Bayesian Approaches to Segmenting a Simple Time Series Monash University 2017 Segmentation 1997 1959.1/36097 Time series Evidence MML monash:6938 Bayes factors Minimum message length 2017-06-05 06:22:23 Journal contribution https://bridges.monash.edu/articles/journal_contribution/Bayesian_Approaches_to_Segmenting_a_Simple_Time_Series/5073745 The segmentation problem arises in many applications in data mining, A.I. and statistics. In this paper, we consider segmenting simple time series. We develop two Bayesian approaches for segmenting a time series, namely the Bayes Factor approach, and the Minimum Message Length (MML) approach. We perform simulations comparing these Bayesian approaches, and then perform a comparison with other classical approaches, namely AIC, MDL and BIC. We conclude that the MML criterion is the preferred criterion. We then apply the segmentation method to financial time series data.