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Biometrika 1997 84(2):489-494; doi:10.1093/biomet/84.2.489
© 1997 by Biometrika Trust
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MISCELLANEA

Time series decomposition

MIKE WEST

Institute of Statistics and Decision Sciences, Duke University Durham, North Carolina 27708-0251, U.S.A. http://www.statduke.edu

A constructive result on time series decomposition is presented and illustrated. Developed through dynamic linear models, the decomposition is useful in analysis of an observed time series through inference about underlying, latent component series that may have physical interpretations. Particular special cases include state space autoregressive component models, in which the decomposition is useful for isolating latent, quasi-cyclical components, in particular. Brief summaries of analyses of some geological records related to climatic change illustrate the result.

Key Words: Autoregressive process • Bayesian computation • Dynamic linear model • Quasi-periodic component • State space model • Time-varying cycle


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