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6 - Time Series Models

Published online by Cambridge University Press:  11 May 2024

John H. Maindonald
Affiliation:
Statistics Research Associates, Wellington, New Zealand
W. John Braun
Affiliation:
University of British Columbia, Okanagan
Jeffrey L. Andrews
Affiliation:
University of British Columbia, Okanagan
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Summary

Common time series models allow for a correlation between observations that is likely to be largest for points that are close together in time. Adjustments can be made, also, for seasonal effects. Variation in a single spatial dimension may have characteristics akin to those of time series, and comparable models find application there. Autoregressive models, which make good intuitive sense and are simple to describe, are the starting point for discussion; then moving on to autoregressive moving average with possible differencing. The "forecast" package for R has mechanisms that allow automatic selection of model parameters. Exponential smoothing state space (exponential time series or ETS) models are an important alternative that have often proved effective in forecasting applications. ARCH and GARCH heteroskedasticity models are further classes that have been developed to handle the special characteristics of financial time series.

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A Practical Guide to Data Analysis Using R
An Example-Based Approach
, pp. 292 - 317
Publisher: Cambridge University Press
Print publication year: 2024

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  • Time Series Models
  • John H. Maindonald, Statistics Research Associates, Wellington, New Zealand, W. John Braun, University of British Columbia, Okanagan, Jeffrey L. Andrews, University of British Columbia, Okanagan
  • Book: A Practical Guide to Data Analysis Using R
  • Online publication: 11 May 2024
  • Chapter DOI: https://doi.org/10.1017/9781009282284.007
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  • Time Series Models
  • John H. Maindonald, Statistics Research Associates, Wellington, New Zealand, W. John Braun, University of British Columbia, Okanagan, Jeffrey L. Andrews, University of British Columbia, Okanagan
  • Book: A Practical Guide to Data Analysis Using R
  • Online publication: 11 May 2024
  • Chapter DOI: https://doi.org/10.1017/9781009282284.007
Available formats
×

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 H. Maindonald, Statistics Research Associates, Wellington, New Zealand, W. John Braun, University of British Columbia, Okanagan, Jeffrey L. Andrews, University of British Columbia, Okanagan
  • Book: A Practical Guide to Data Analysis Using R
  • Online publication: 11 May 2024
  • Chapter DOI: https://doi.org/10.1017/9781009282284.007
Available formats
×