Part of the Use R book series (USE R)

Once we have identified any trend and seasonal effects, we can deseasonalise the time series and remove the trend. If we use the additive decomposition method of §1.5, we first calculate the seasonally adjusted time series and then remove the trend by subtraction. This leaves the random component, but the random component is not necessarily well modelled by independent random variables. In many cases, consecutive variables will be correlated. If we identify such correlations, we can improve our forecasts, quite dramatically if the correlations are high. We also need to estimate correlations if we are to generate realistic time series for simulations. The correlation structure of a time series model is defined by the correlation function, and we estimate this from the observed time series.


Time Series Wave Height Time Series Model Random Component Linear Association 


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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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