Multi-scale and hidden resolution time series models

Published

Journal Article

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. © 2006 International Society for Bayesian Analysis.

Full Text

Duke Authors

Cited Authors

  • Ferreira, MAR; West, M; Lee, HKH; Higdon, DM

Published Date

  • December 1, 2006

Published In

Volume / Issue

  • 1 / 4

Start / End Page

  • 947 - 968

Electronic International Standard Serial Number (EISSN)

  • 1931-6690

International Standard Serial Number (ISSN)

  • 1936-0975

Digital Object Identifier (DOI)

  • 10.1214/06-BA131

Citation Source

  • Scopus