Bayesian Analysis

Multi-scale and hidden resolution time series models

Marco A. R. Ferreira, David M. Higdon, Herbert K. H. Lee, and Mike West

Full-text: Open access


We introduce a class of multi-scale models for time series. The novel framework couples standard linear models at different levels of resolution via stochastic links across scales. Jeffrey's rule of conditioning is used to revise the implied distributions and ensure that the probability distributions at the different levels are strictly compatible. This results in a new class of models for time series with three key characteristics: this class exhibits a variety of autocorrelation structures based on a parsimonious parameterization, it has the ability to combine information across levels of resolution, and it also has the capacity to emulate long memory processes. The potential applications of such multi-scale models include problems in which it is of interest to develop consistent stochastic models across time-scales and levels of resolution, in order to coherently combine and integrate information arising at different levels of resolution. Bayesian estimation based on MCMC analysis and forecasting based on simulation are developed. One application to the analysis of the flow of a river illustrates the new class of models and its utility.

Article information

Bayesian Anal. Volume 1, Number 4 (2006), 947-967.

First available in Project Euclid: 22 June 2012

Permanent link to this document

Digital Object Identifier

Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Autoregressive models Bayesian inference Combination of multi-resolution information Jeffrey's rule of conditioning Multi-scale stochastic models Multi-scale time series models


Ferreira, Marco A. R.; West, Mike; Lee, Herbert K. H.; Higdon, David M. Multi-scale and hidden resolution time series models. Bayesian Anal. 1 (2006), no. 4, 947--967. doi:10.1214/06-BA131.

Export citation