13 Resources

R code for all chapters

These files include all the R code that is used in the book. It is easier to use these than copy and paste from within each chapter.

Solutions to exercises

These are password protected and only available to instructors. Please request the password from OTexts support centre. You will need to provide evidence that you are an instructor and not a student (e.g., a link to your personal page on a university website).

Forecasting time series using R

Here is a one-hour talk by one of the authors on Forecasting time series using R. The URL of the book mentioned at the end has changed to www.otexts.org/fpp/, the site you are now on.

Time series course

These are the slides from a course on Time series forecasting using R. Each lecture lasted one hour.

  1. Introduction to forecasting [Exercises]
  2. The forecaster's toolbox [Exercises]
  3. Autocorrelation and seasonality [Exercises]
  4. White noise and time series decomposition [Exercises]
  5. Exponential smoothing methods [Exercises]
  6. ETS models [Exercises]
  7. Transformations and adjustments [Exercises]
  8. Stationarity and differencing [Exercises]
  9. Non-seasonal ARIMA models [Exercises]
  10. Seasonal ARIMA models [Exercises]
  11. Dynamic regression [Exercises]
  12. Advanced methods

Predictive Analytics course (University of Sydney)

Slides contributed by Andrey Vasnev

  1. Section 2.5: Evaluating forecast accuracy
  2. Chapter 4: Simple regression
  3. Chapter 5: Multiple regression

Economic Forecasting course (University of Hawaii)

Slides contributed by Peter Fuleky

  1. Using R. (Rmd source)
  2. Getting started. (Rmd source)
  3. The forecaster's toolbox. (Rmd source)
  4. Judgemental forecasts. (Rmd source)
  5. Simple regression. (Rmd source)
  6. Multiple regression. (Rmd source)
  7. Time series decomposition. (Rmd source)
  8. Exponential smoothing. (Rmd source)
  9. ARIMA models. (Rmd source)

Test bank

(Contributed by Pasha Safarzadeh)

Download word file

Case study: Planning and forecasting in a volatile setting

By Amy Wheeler, Nina Weitkamp, Patrick Berlekamp, Johannes Brauer, Andreas Faatz and Hans-Ulrich Holst
Designed and coded at Hochschule Osnabrück, Germany
Contact: Andreas Faatz

Download data and R code