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Basic Stochastic Models

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

So far, we have considered two approaches for modelling time series. The first is based on an assumption that there is a fixed seasonal pattern about a trend.We can estimate the trend by local averaging of the deseasonalised data, and this is implemented by the R function decompose. The second approach allows the seasonal variation and trend, described in terms of a level and slope, to change over time and estimates these features by exponentially weighted averages. We used the HoltWinters function to demonstrate this method.

Keywords

Random Walk White Noise Time Series Model Shift Operator Random Walk Model 
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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