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Decomposition of time series models in state-space form

Lookup NU author(s): Dr Kostas Triantafyllopoulos

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Abstract

A methodology is proposed for decompositions of a very wide class of time series, including normal and non-normal time series, which are represented in state-space form. In particular the linked signals generated from dynamic generalized linear models are decomposed into a suitable sum of noise-free dynamic linear models. A number of relevant general results are given and two important cases, consisting of normally distributed data and binomially distributed data, are examined in detail. The methods are illustrated by considering examples involving both linear trend and seasonal component time series.

Publication metadata

Author(s): Godolphin EJ, Triantafyllopoulos K

Publication type: Article

Publication status: Published

Journal: Computational Statistics and Data Analysis

Year: 2006

Volume: 50

Issue: 9

Pages: 2232-2246

ISSN (print): 0167-9473

ISSN (electronic): 1872-7352

Publisher: Elsevier BV

URL: http://dx.doi.org/10.1016/j.csda.2004.12.012

DOI: 10.1016/j.csda.2004.12.012

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