Bayesian multiscale analysis for time series data

Authors:
Tor Arne Øigård;Håvard Rue;Fred Godtliebsen
Affiliations:
Department of Physics and Technology, University of Tromsø, NO-9037 Tromsø, Norway;Department of Mathematical Sciences, The Norwegian University for Science and Technology, NO-7491 Trondheim, Norway;Department of Statistics, University of Tromsø, NO-9037 Tromsø, Norway
Venue:
Computational Statistics & Data Analysis
Year:
2006

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Abstract

A recently proposed Bayesian multiscale tool for exploratory analysis of time series data is reconsidered and umerous important improvements are suggested. The improvements are in the model itself, the algorithms to analyse it, and how to display the results. The consequence is that exact results can be obtained in real time using only a tiny fraction of the CPU time previously needed to get approximate results. Analysis of both real and synthetic data are given to illustrate our new approach. Multiscale analysis for time series data is a useful tool in applied time series analysis, and with the new model and algorithms, it is also possible to do such analysis in real time.