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

Trends in time series can be classified as stochastic or deterministic. We may consider a trend to be stochastic when it shows inexplicable changes in direction, and we attribute apparent transient trends to high serial correlation with random error. Trends of this type, which are common in financial series, can be simulated in R using models such as the random walk or autoregressive process (Chapter 4). In contrast, when we have some plausible physical explanation for a trend we will usually wish to model it in some deterministic manner. For example, a deterministic increasing trend in the data may be related to an increasing population, or a regular cycle may be related to a known seasonal frequency. Deterministic trends and seasonal variation can be modelled using regression.


Ordinary Little Square Temperature Series Generalise Little Square Quadratic Trend Residual Series 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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

Personalised recommendations