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State Space Models

  • Paul S.P. Cowpertwait
  • Andrew V. Metcalfe
Chapter
Part of the Use R book series (USE R)

The state space formulation for time series models is quite general and encompasses most of the models we have considered so far. However, it is usually simpler to use the specific time series models we have already introduced when they are appropriate for the physical situation. Here, we shall focus on applications for which we require parameters to adapt over time, and to do so more quickly than in a Holt-Winters model. The recent turmoil on the world’s stock exchanges is a dramatic reminder that time series are subject to sudden changes. Another desirable feature of state space models is that they can incorporate time series of predictor variables in a straightforward manner.

Keywords

Forecast Error Time Series Model State Space Model Multivariate Normal Distribution Closing Price 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag New York 2009

Authors and Affiliations

  1. 1.Inst. Information and Mathematical Sciences, Maasey UniversityAuckland, Albany CampusNew Zealand
  2. 2.School of Mathematical Sciences, University of AdelaideAustralia

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