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.