Bayesian Approaches to Segmenting A Simple Time Series

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.

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Year of publication:	1997
Authors:	Oliver, J.J. ; Forbes, C.S.
Institutions:	Department of Econometrics and Business Statistics, Monash Business School
Subject:	ECONOMETRICS \| TIME SERIES

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Series:	Monash Econometrics and Business Statistics Working Papers.
Type of publication:	Book / Working Paper
Notes:	Number 14/97 20 pages
Classification:	C11 - Bayesian Analysis ; C22 - Time-Series Models
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005149025