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Title: Forecasting: Principles and Practice 3rd ed - 99-back-matter
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# Translations

### Second edition

A [**Chinese translation**](https://otexts.com/fppcn) is available, thanks to [Professor Yanfei Kang](https://yanfei.site/) and [Professor Feng Li](https://feng.li/), and their students.

A [**Korean translation**](https://otexts.com/fppkr) is available, thanks to [Dr Daniel Young Ho Kim](http://danielykim.me/).

### Third edition

A [**Chinese translation**](https://otexts.com/fpp3cn) is available, thanks to [Professor Yanfei Kang](https://yanfei.site/) and [Professor Feng Li](https://feng.li/), and their students.

A [**Greek translation**](https://otexts.com/fppgr) is available, thanks to [Dr Ioannis Nikas](http://tourism.upatras.gr/nikas/) and [Dr Athanasios Koutras](https://thanasiskoutras.com).

An [**Italian translation**](https://otexts.com/fppit) is available, thanks to [Professor Domenico Vistocco](http://domenicovistocco.it/) and [Professor Tommaso Di Fonzo](https://homes.stat.unipd.it/tommasodifonzo/), and their colleagues and students at the University of Naples Federico II and the University of Padua.

A [**Japanese translation**](https://otexts.com/fppjp) is available, thanks to [Mitsuo Shiota](https://mitsuoxv.rbind.io/) and [Professor Tomoo Inoue](https://www-cv01.ufinity.jp/seikei/cvclients/researchers/tomoo-inoue?frame_id=348&lang=en).

A [**Portuguese translation**](https://otexts.com/fpppg) is available, thanks to Matheus Henrique Dal Molin Ribeiro, Gilson Adamczuk Oliveira, Bruno Luis Barbosa Cavalcante, José Donizetti de Lima, Manuel Pereira Lopes, Sandra Cristina De Faria Ramos, Ricardo Puziol de Oliveira, amd Tatiane Teixeira Leal.

A [**Russian translation**](https://dmkpress.com/catalog/computer/data/978-5-93700-151-1/) is available in print from DMK Press.

A [**Spanish translation**](https://otexts.com/fppsp) is available, thanks to [José Manuel Benítez Sánchez](https://scholar.google.com/citations?user=1iSTbIkAAAAJ&hl=en).

### In progress

Translations into Farsi, French, German, Tamil, Turkish, and Arabic, are already underway. If you think you can help with any of these, please let Rob Hyndman know.

If anyone is interested in creating a translation of the book into another language, please contact Rob Hyndman.

# About the authors

![](https://otexts.com/fpp3/figs/Hyndman.png) [**Rob J Hyndman**](https://robjhyndman.com) is the Vice-Chancellor’s Distinguished Professor of Statistics in the Department of Econometrics and Business Statistics at Monash University, Australia. He is author of 6 books and over 250 research papers, and an elected Fellow of the Australian Academy of Science, the Academy for the Social Sciences in Australia, and the International Institute of Forecasters. He was Editor-in-Chief of the *International Journal of Forecasting* from 2005 to 2018. For over 40 years, Rob has maintained an active consulting practice, assisting hundreds of companies and organisations on forecasting problems. He has won awards for his research, teaching, consulting and graduate supervision.

![](https://otexts.com/fpp3/figs/Athanasopoulos.png) [**George Athanasopoulos**](https://research.monash.edu/en/persons/george-athanasopoulos) is a Professor and Head of the Department of Econometrics and Business Statistics at Monash University, Australia. He has been a Director of the International Institute of Forecasters (IIF) since 2014, and was President from 2020 to 2024. George has received multiple awards and distinctions for his research and teaching. He is on the Editorial Boards of the *Journal of Travel Research* and the *International Journal of Forecasting*.

# Buy a print version

![](https://otexts.com/fpp3/figs/fpp3_front_cover.jpg) | Paperback [Amazon Australia](https://www.amazon.com.au/dp/0987507133?tag=otexts04-22)  [Amazon Canada](https://www.amazon.ca/dp/0987507133?tag=otexts08-20)  [Amazon France](https://www.amazon.fr/dp/0987507133?tag=otexts0e-21)  [Amazon Germany](https://www.amazon.de/dp/0987507133?tag=otexts0c-21)  [Amazon Italy](https://www.amazon.it/dp/0987507133?tag=otexts08-21)  [Amazon Japan](https://www.amazon.co.jp/dp/0987507133?tag=otexts-22)  [Amazon Spain](https://www.amazon.es/dp/0987507133?tag=otexts07-21)  [Amazon UK](https://www.amazon.co.uk/dp/0987507133?tag=wwwotextsorg-21)  [Amazon USA](https://www.amazon.com/dp/0987507133?tag=otexts-20)  *(As an Amazon Associate, OTexts earns from purchases obtained by clicking these links.)* |

# Help and feedback

**Got a problem with *Forecasting: Principles and Practice*?**

If you’re looking for help with your forecasting, please ask on the [OTexts discussion forum](https://github.com/orgs/OTexts/discussions). If you’ve found an error in the book (even a tiny typo), you can [let us know](https://github.com/orgs/OTexts/discussions/categories/error-report) on the forum.

# Changelog

**Changes made since the last print edition (2021)**

* YouTube videos added to start of many sections.
* Typos fixed, and some wording improved for clarification or accuracy.
* Switched pipe from `%>%` to `|>`.
* Packages updated to latest CRAN versions.
* `ggtime` package now used for graphics (instead of `feasts`)
* Code updated to ensure it works with latest CRAN packages, and to use new features of some packages.

### Preface

* Link to YouTube playlist added.
* Link to discussion forum added.

### Chapter 1

* Corrected description of Babylonian sheep liver forecasting (was “distribution of maggots in a rotten sheep’s liver”; now “appearance of a sheep’s liver”). Thanks to Srikanth Reddy for pointing out the error.
* Corrected statement about Emperor Constantius II, and provided footnote to source.
* Fixed date of the Vagrancy Act and provided a quote and footnote to source.

### Chapter 2

* **Section 2.10**: Merged two exercises (now Exercise 1).

### Chapter 3

* **Section 3.6**: Added reference to Bandara et al. ([2025](#ref-mstl)).

### Chapter 5

* **Section 5.4**: Discussion of portmanteau tests no longer uses degrees of freedom based on model parameters, except for ARIMA models.
* **Section 5.5**: Corrected the residual standard deviation formula to include \(M\) (the number of missing residuals) in the denominator; corrected the drift method forecast standard deviation formula.
* **Section 5.5**: Clarified the bootstrapped prediction intervals section, introducing \(y^\*\) notation to distinguish simulated from observed values.

### Chapter 9

* **Section 9.1**: Updated explanation of KPSS unit root test p-values.
* **Section 9.7**: Added subsection on portmanteau tests of residuals for ARIMA models.
* **Section 9.11**: Removed Exercise 17 (which used Quandl).

### Chapter 13

* **Section 13.2**: Added a reference to Syntetos & Boylan ([2001](#ref-SB01)).
* **Section 13.4**: Added a reference to Wang et al. ([2023](#ref-combinations)).

### Appendix: For instructors

* All solutions rewritten using quarto.
* Slides used in videos added.
* Past exams added.
* Link to Python edition added.

### Translations

* Page added.

### About the authors

* Updated photos and bios.

### Buy a print version

* Updated to include many more Amazon sites.

### Help and feedback

* Form removed and link added to discussion forum.

### Changelog

* Page added.

### Bibliography

* Added Bandara et al. ([2025](#ref-mstl)).
* Updated Panagiotelis et al. ([2023](#ref-PanEtAl2020_Probabilistic)).
* Added Syntetos & Boylan ([2001](#ref-SB01)).
* Added Wang et al. ([2023](#ref-combinations)).
* Added DOI or Amazon links to bibliography entries where available.

### Bibliography

Bandara, K., Hyndman, R. J., & Bergmeir, C. (2025). MSTL: A seasonal-trend decomposition algorithm for time series with multiple seasonal patterns. *International J Operational Research*, *52*(1).

Panagiotelis, A., Gamakumara, P., Athanasopoulos, G., & Hyndman, R. J. (2023). Probabilistic forecast reconciliation: Properties, evaluation and score optimisation. *European J Operational Research*, *306*(2), 693–706.

Syntetos, A. A., & Boylan, J. E. (2001). On the bias of intermittent demand estimates. *International Journal of Production Economics*, *71*, 457–466.

Wang, X., Hyndman, R. J., Li, F., & Kang, Y. (2023). Forecast combinations: An over 50-year review. *International J Forecasting*, *39*(4), 1518–1547.

# Bibliography

Anscombe, F. J. (1973). Graphs in statistical analysis. *The American Statistician*, *27*(1), 17–21.

Armstrong, J. S. (1978). *Long-range forecasting: From crystal ball to computer*. John Wiley & Sons.

Armstrong, J. S. (Ed.). (2001). *Principles of forecasting: A handbook for researchers and practitioners*. Kluwer Academic Publishers.

Athanasopoulos, G., Ahmed, R. A., & Hyndman, R. J. (2009). Hierarchical forecasts for Australian domestic tourism. *International Journal of Forecasting*, *25*, 146–166.

Athanasopoulos, G., Gamakumara, P., Panagiotelis, A., Hyndman, R. J., & Affan, M. (2020). Hierarchical forecasting. In P. Fuleky (Ed.), *Macroeconomic forecasting in the era of big data* (pp. 689–719). Springer.

Athanasopoulos, G., & Hyndman, R. J. (2008). Modelling and forecasting Australian domestic tourism. *Tourism Management*, *29*(1), 19–31.

Athanasopoulos, G., Hyndman, R. J., Kourentzes, N., & Petropoulos, F. (2017). Forecasting with temporal hierarchies. *European Journal of Operational Research*, *262*(1), 60–74.

Athanasopoulos, G., Poskitt, D. S., & Vahid, F. (2012). Two canonical VARMA forms: Scalar component models vis-à-vis the echelon form. *Econometric Reviews*, *31*(1), 60–83.

Bandara, K., Hyndman, R. J., & Bergmeir, C. (2025). MSTL: A seasonal-trend decomposition algorithm for time series with multiple seasonal patterns. *International J Operational Research*, *52*(1).

Bates, J. M., & Granger, C. W. J. (1969). The combination of forecasts. *Operational Research Quarterly*, *20*(4), 451–468.

Bergmeir, C., Hyndman, R. J., & Benítez, J. M. (2016). Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation. *International Journal of Forecasting*, *32*(2), 303–312.

Bergmeir, C., Hyndman, R. J., & Koo, B. (2018). A note on the validity of cross-validation for evaluating autoregressive time series prediction. *Computational Statistics and Data Analysis*, *120*, 70–83.

Bickel, P. J., & Doksum, K. A. (1981). An analysis of transformations revisited. *Journal of the American Statistical Association*, *76*(374), 296–311.

Box, G. E. P., & Cox, D. R. (1964). An analysis of transformations. *Journal of the Royal Statistical Society. Series B, Statistical Methodology*, *26*(2), 211–252.

Box, G. E. P., & Jenkins, G. M. (1970). *Time series analysis: Forecasting and control*. Holden-Day.

Box, G. E. P., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). *Time series analysis: Forecasting and control* (5th ed). John Wiley & Sons.

Brockwell, P. J., & Davis, R. A. (2016). *Introduction to time series and forecasting* (3rd ed). Springer.

Brown, R. G. (1959). *Statistical forecasting for inventory control*. McGraw/Hill.

Buehler, R., Messervey, D., & Griffin, D. (2005). Collaborative planning and prediction: Does group discussion affect optimistic biases in time estimation? *Organizational Behavior and Human Decision Processes*, *97*(1), 47–63.

Christou, V., & Fokianos, K. (2015). On count time series prediction. *Journal of Statistical Computation and Simulation*, *85*(2), 357–373.

Clemen, R. (1989). Combining forecasts: A review and annotated bibliography. *International Journal of Forecasting*, *5*(4), 559–583.

Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. J. (1990). STL: A seasonal-trend decomposition procedure based on loess. *Journal of Official Statistics*, *6*(1), 3–33.

Cleveland, W. S. (1993). *Visualizing data*. Hobart Press.

Croston, J. D. (1972). Forecasting and stock control for intermittent demands. *Operational Research Quarterly*, *23*(3), 289–303.

Dagum, E. B., & Bianconcini, S. (2016). *Seasonal adjustment methods and real time trend-cycle estimation*. Springer.

Eroglu, C., & Croxton, K. L. (2010). Biases in judgmental adjustments of statistical forecasts: The role of individual differences. *International Journal of Forecasting*, *26*(1), 116–133.

Fan, S., & Hyndman, R. J. (2012). Short-term load forecasting based on a semi-parametric additive model. *IEEE Transactions on Power Systems*, *27*(1), 134–141.

Fildes, R., & Goodwin, P. (2007a). Against your better judgment? How organizations can improve their use of management judgment in forecasting. *Interfaces*, *37*(6), 570–576.

Fildes, R., & Goodwin, P. (2007b). Good and bad judgment in forecasting: Lessons from four companies. *Foresight: The International Journal of Applied Forecasting*, *8*, 5–10.

Franses, P. H., & Legerstee, R. (2013). Do statistical forecasting models for SKU-level data benefit from including past expert knowledge? *International Journal of Forecasting*, *29*(1), 80–87.

Gardner, E. S. (1985). Exponential smoothing: The state of the art. *Journal of Forecasting*, *4*(1), 1–28.

Gardner, E. S. (2006). Exponential smoothing: The state of the art — Part II. *International Journal of Forecasting*, *22*, 637–666.

Gardner, E. S., & McKenzie, E. (1985). Forecasting trends in time series. *Management Science*, *31*(10), 1237–1246.

Gneiting, T., & Katzfuss, N. (2014). Probabilistic forecasting. *Annual Review of Statistics and Its Application*, *1*(1), 125–151.

Goodwin, P., & Wright, G. (2009). *Decision analysis for management judgment* (4th ed). John Wiley & Sons.

Green, K. C., & Armstrong, J. S. (2007). Structured analogies for forecasting. *International Journal of Forecasting*, *23*(3), 365–376.

Gross, C. W., & Sohl, J. E. (1990). Disaggregation methods to expedite product line forecasting. *Journal of Forecasting*, *9*, 233–254.

Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). *Survey methodology* (2nd ed). John Wiley & Sons.

Guerrero, V. M. (1993). Time-series analysis supported by power transformations. *Journal of Forecasting*, *12*(1), 37–48.

Hamilton, J. D. (1994). *Time series analysis*. Princeton University Press, Princeton.

Harrell, F. E. (2015). *Regression modeling strategies: With applications to linear models, logistic and ordinal regression, and survival analysis* (2nd ed). Springer.

Harris, R., & Sollis, R. (2003). *Applied time series modelling and forecasting*. John Wiley & Sons.

Harvey, N. (2001). Improving judgment in forecasting. In J. S. Armstrong (Ed.), *Principles of forecasting: A handbook for researchers and practitioners* (pp. 59–80). Kluwer Academic Publishers.

Hewamalage, H., Bergmeir, C., & Bandara, K. (2021). Recurrent neural networks for time series forecasting: Current status and future directions. *International Journal of Forecasting*, *37*(1), 388–427.

Holt, C. C. (1957). *Forecasting seasonals and trends by exponentially weighted averages* (ONR Memorandum No. 52). Carnegie Institute of Technology, Pittsburgh USA. Reprinted in the *International Journal of Forecasting*, 2004.

Hyndman, R. J., Ahmed, R. A., Athanasopoulos, G., & Shang, H. L. (2011). Optimal combination forecasts for hierarchical time series. *Computational Statistics and Data Analysis*, *55*(9), 2579–2589.

Hyndman, R. J., & Fan, S. (2010). Density forecasting for long-term peak electricity demand. *IEEE Transactions on Power Systems*, *25*(2), 1142–1153.

Hyndman, R. J., & Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. *Journal of Statistical Software*, *27*(1), 1–22.

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. *International Journal of Forecasting*, *22*(4), 679–688.

Hyndman, R. J., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2008). *Forecasting with exponential smoothing: The state space approach*. Springer-Verlag.

Hyndman, R. J., Wang, E., & Laptev, N. (2015). Large-scale unusual time series detection. *Proceedings of the IEEE International Conference on Data Mining*, 1616–1619.

Izenman, A. J. (2008). *Modern multivariate statistical techniques: Regression, classification and manifold learning*. Springer.

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). *An introduction to statistical learning: With applications in R*. Springer.

Kahn, K. B. (2006). *New product forecasting: An applied approach*. M.E. Sharp.

Kahneman, D., & Lovallo, D. (1993). Timid choices and bold forecasts: A cognitive perspective on risk taking. *Management Science*, *39*(1), 17–31.

Kang, Y., Hyndman, R. J., & Smith-Miles, K. (2017). Visualising forecasting algorithm performance using time series instance spaces. *International Journal of Forecasting*, *33*(2), 345–358.

Kourentzes, N., & Athanasopoulos, G. (2019). Cross-temporal coherent forecasts for Australian tourism. *Annals of Tourism Research*, *75*, 393–409.

Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? *Journal of Econometrics*, *54*(1-3), 159–178.

Lahiri, S. N. (2003). *Resampling methods for dependent data*. Springer Science & Business Media.

Lawrence, M., Goodwin, P., O’Connor, M., & Önkal, D. (2006). Judgmental forecasting: A review of progress over the last 25 years. *International Journal of Forecasting*, *22*(3), 493–518.

Lütkepohl, H. (2007). General-to-specific or specific-to-general modelling? An opinion on current econometric terminology. *Journal of Econometrics*, *136*(1), 234–319.

Morwitz, V. G., Steckel, J. H., & Gupta, A. (2007). When do purchase intentions predict sales? *International Journal of Forecasting*, *23*(3), 347–364.

Önkal, D., Sayım, K. Z., & Gönül, M. S. (2013). Scenarios as channels of forecast advice. *Technological Forecasting and Social Change*, *80*(4), 772–788.

Ord, J. K., Fildes, R., & Kourentzes, N. (2017). *Principles of business forecasting* (2nd ed.). Wessex Press Publishing Co.

Panagiotelis, A., Athanasopoulos, G., Gamakumara, P., & Hyndman, R. J. (2021). Forecast reconciliation: A geometric view with new insights on bias correction. *International Journal of Forecasting*, *37*(1), 343–359.

Panagiotelis, A., Gamakumara, P., Athanasopoulos, G., & Hyndman, R. J. (2023). Probabilistic forecast reconciliation: Properties, evaluation and score optimisation. *European J Operational Research*, *306*(2), 693–706.

Pankratz, A. E. (1991). *Forecasting with dynamic regression models*. John Wiley & Sons.

Pegels, C. C. (1969). Exponential forecasting: Some new variations. *Management Science*, *15*(5), 311–315.

Peña, D., Tiao, G. C., & Tsay, R. S. (Eds.). (2001). *A course in time series analysis*. John Wiley & Sons.

Pfaff, B. (2008). *Analysis of integrated and cointegrated time series with R*. Springer Science & Business Media.

Randall, D. M., & Wolff, J. A. (1994). The time interval in the intention-behaviour relationship: Meta-analysis. *British Journal of Social Psychology*, *33*(4), 405–418.

Rowe, G. (2007). A guide to Delphi. *Foresight: The International Journal of Applied Forecasting*, *8*, 11–16.

Rowe, G., & Wright, G. (1999). The Delphi technique as a forecasting tool: Issues and analysis. *International Journal of Forecasting*, *15*(4), 353–375.

Sanders, N., Goodwin, P., Önkal, D., Gönül, M. S., Harvey, N., Lee, A., & Kjolso, L. (2005). When and how should statistical forecasts be judgmentally adjusted? *Foresight: The International Journal of Applied Forecasting*, *1*(1), 5–23.

Sheather, S. J. (2009). *A modern approach to regression with R*. Springer.

Shenstone, L., & Hyndman, R. J. (2005). Stochastic models underlying Croston’s method for intermittent demand forecasting. *Journal of Forecasting*, *24*(6), 389–402.

Syntetos, A. A., & Boylan, J. E. (2001). On the bias of intermittent demand estimates. *International Journal of Production Economics*, *71*, 457–466.

Taylor, J. W. (2003). Exponential smoothing with a damped multiplicative trend. *International Journal of Forecasting*, *19*(4), 715–725.

Taylor, S. J., & Letham, B. (2018). Forecasting at scale. *The American Statistician*, *72*(1), 37–45.

Theodosiou, M. (2011). Forecasting monthly and quarterly time series using STL decomposition. *International Journal of Forecasting*, *27*(4), 1178–1195.

Unwin, A. (2015). *Graphical data analysis with R*. Chapman; Hall/CRC.

Wang, X., Hyndman, R. J., Li, F., & Kang, Y. (2023). Forecast combinations: An over 50-year review. *International J Forecasting*, *39*(4), 1518–1547.

Wang, X., Smith, K. A., & Hyndman, R. J. (2006). Characteristic-based clustering for time series data. *Data Mining and Knowledge Discovery*, *13*(3), 335–364.

Wickramasuriya, S. L., Athanasopoulos, G., & Hyndman, R. J. (2019). Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization. *Journal of the American Statistical Association*, *114*(526), 804–819.

Winkler, R. L. (1972). A decision-theoretic approach to interval estimation. *Journal of the American Statistical Association*, *67*(337), 187–191.

Winters, P. R. (1960). Forecasting sales by exponentially weighted moving averages. *Management Science*, *6*(3), 324–342.

Young, P. C., Pedregal, D. J., & Tych, W. (1999). Dynamic harmonic regression. *Journal of Forecasting*, *18*, 369–394.
