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9 - Time series models

Published online by Cambridge University Press:  05 October 2013

John Maindonald
Affiliation:
Australian National University, Canberra
W. John Braun
Affiliation:
University of Western Ontario
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Summary

A time series is a sequence of observations that have been recorded over time. Almost invariably, observations that are close together in time are more strongly correlated than observations that are widely separated. The independence assumption of previous chapters is, in general, no longer valid. Variation in a single spatial dimension may have characteristics akin to those of time series, and the same types of models may find application there also.

Many techniques have been developed to deal with the special nature of the dependence that is commonly found in such series. The present treatment will be introductory and restricted in scope, focusing on autoregressive integrated moving average (ARIMA) models.

In the section that follows, annual depth measurements at a specific site on Lake Huron will be modeled directly as an ARIMA process. Section 9.2 will model a regression where the error term has a time series structure. The chapter will close with a brief discussion of “non-linear” time series, such as have been widely used in modeling financial time series.

The analyses will use functions in the stats package, which is a recommended package, included with binary distributions. Additionally, there will be use of the forecast package. In order to make this available, install the forecasting bundle of packages.

The brief discussion of non-linear time series (ARCH and GARCH models) will require access to the tseries package, which must be installed.

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Data Analysis and Graphics Using R
An Example-Based Approach
, pp. 283 - 302
Publisher: Cambridge University Press
Print publication year: 2010

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  • Time series models
  • John Maindonald, Australian National University, Canberra, W. John Braun, University of Western Ontario
  • Book: Data Analysis and Graphics Using R
  • Online publication: 05 October 2013
  • Chapter DOI: https://doi.org/10.1017/CBO9781139194648.012
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  • Time series models
  • John Maindonald, Australian National University, Canberra, W. John Braun, University of Western Ontario
  • Book: Data Analysis and Graphics Using R
  • Online publication: 05 October 2013
  • Chapter DOI: https://doi.org/10.1017/CBO9781139194648.012
Available formats
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Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

  • Time series models
  • John Maindonald, Australian National University, Canberra, W. John Braun, University of Western Ontario
  • Book: Data Analysis and Graphics Using R
  • Online publication: 05 October 2013
  • Chapter DOI: https://doi.org/10.1017/CBO9781139194648.012
Available formats
×