ml_debug/docs/evidence/fpp3/99-back-matter.md

Source: https://otexts.com/fpp3/translations.html (chapter translations, 6 section pages merged) Title: Forecasting: Principles and Practice 3rd ed - 99-back-matter Fetched-via: urllib + markitdown (content div.page-inner section.normal), 2026-07-26 Fetch-status: full content; images/links point to absolute otexts.com URLs

Translations

Second edition

A Chinese translation is available, thanks to Professor Yanfei Kang and Professor Feng Li, and their students.

A Korean translation is available, thanks to Dr Daniel Young Ho Kim.

Third edition

A Chinese translation is available, thanks to Professor Yanfei Kang and Professor Feng Li, and their students.

A Greek translation is available, thanks to Dr Ioannis Nikas and Dr Athanasios Koutras.

An Italian translation is available, thanks to Professor Domenico Vistocco and Professor Tommaso Di Fonzo, and their colleagues and students at the University of Naples Federico II and the University of Padua.

A Japanese translation is available, thanks to Mitsuo Shiota and Professor Tomoo Inoue.

A Portuguese translation 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 is available in print from DMK Press.

A Spanish translation is available, thanks to José Manuel Benítez Sánchez.

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

Rob J Hyndman 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.

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.

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Help and feedback

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If you’re looking for help with your forecasting, please ask on the OTexts discussion forum. If you’ve found an error in the book (even a tiny typo), you can let us know 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).

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).
• Section 13.4: Added a reference to Wang et al. (2023).

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).
• Updated Panagiotelis et al. (2023).
• Added Syntetos & Boylan (2001).
• Added Wang et al. (2023).
• 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.

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