Forecasting: Principles and Practice
(2nd ed)
Rob J Hyndman and George Athanasopoulos
Monash University, Australia
Preface
This is the second edition of Forecasting: Principles & Practice, which uses the
forecast package in R. The third edition, which uses the fable package, is also
available.
Welcome to our online textbook on forecasting.
This textbook is intended to provide a comprehensive introduction to forecasting
methods and to present enough information about each method for readers to be able
to use them sensibly. We don’t attempt to give a thorough discussion of the theoretical
details behind each method, although the references at the end of each chapter will fill
in many of those details.
The book is written for three audiences: (1) people finding themselves doing forecasting
in business when they may not have had any formal training in the area;
(2) undergraduate students studying business; (3) MBA students doing a forecasting
elective. We use it ourselves for a third-year subject for students undertaking a
Bachelor of Commerce or a Bachelor of Business degree at Monash University,
Australia.
For most sections, we only assume that readers are familiar with introductory
statistics, and with high-school algebra. There are a couple of sections that also require
knowledge of matrices, but these are flagged.
At the end of each chapter we provide a
list of “further reading”. In general,
these lists comprise suggested textbooks
that provide a more advanced or detailed
treatment of the subject. Where there is
no suitable textbook, we suggest journal
articles that provide more information.
We use R throughout the book and we
intend students to learn how to forecast
with R. R is free and available on almost
every operating system. It is a wonderful
tool for all statistical analysis, not just
for forecasting. See the Using R appendix
for instructions on installing and using
R.
All R examples in the book assume you
have loaded the fpp2 package, available
on CRAN, using library(fpp2) . This will
automatically load several other
packages including forecast and ggplot2,
Buy a print or downloadable version
as well as all the data used in the book.
We have used v2.5 of the fpp2 package and v8.21 of the forecast package in preparing
this book. These can be installed from CRAN in the usual way. Earlier versions of the
packages will not necessarily give the same results as those shown in this book.
We will use the ggplot2 package for all graphics. If you want to learn how to modify the
graphs, or create your own ggplot2 graphics that are different from the examples
shown in this book, please either read the ggplot2 book (Wickham, 2016), or do the
ggplot2 course on the DataCamp online learning platform.
There is also a DataCamp course based on this book which provides an introduction to
some of the ideas in Chapters 2, 3, 7 and 8, plus a brief glimpse at a few of the topics in
Chapters 9 and 11.
The book is different from other forecasting textbooks in several ways.
It is free and online, making it accessible to a wide audience.
It uses R, which is free, open-source, and extremely powerful software.
The online version is continuously updated. You don’t have to wait until the next
edition for errors to be removed or new methods to be discussed. We will update the
book frequently.
There are dozens of real data examples taken from our own consulting practice. We
have worked with hundreds of businesses and organisations helping them with
forecasting issues, and this experience has contributed directly to many of the
examples given here, as well as guiding our general philosophy of forecasting.
We emphasise graphical methods more than most forecasters. We use graphs to
explore the data, analyse the validity of the models fitted and present the
forecasting results.
Changes in the second edition
The most important change in edition 2 of the book is that we have restricted our focus
to time series forecasting. That is, we no longer consider the problem of cross-sectional
prediction. Instead, all forecasting in this book concerns prediction of data at future
times using observations collected in the past.
We have also simplified the chapter on exponential smoothing, and added new chapters
on dynamic regression forecasting, hierarchical forecasting and practical forecasting
issues. We have added new material on combining forecasts, handling complicated
seasonality patterns, dealing with hourly, daily and weekly data, forecasting count time
series, and we have many new examples. We have also revised all existing chapters to
bring them up-to-date with the latest research, and we have carefully gone through
every chapter to improve the explanations where possible, to add newer references, to
add more exercises, and to make the R code simpler.
Helpful readers of the earlier versions of the book let us know of any typos or errors
they had found. These were updated immediately online. No doubt we have introduced
some new mistakes, and we will correct them online as soon as they are spotted. Please
continue to let us know about such things.
If you have questions about using the R packages discussed in this book, or about
forecasting in general, please ask on the RStudio Community website.
Happy forecasting!
Rob J Hyndman and George Athanasopoulos
April 2018
To cite the online version of this book, please use the following:
Hyndman, R.J., & Athanasopoulos, G. (2018) Forecasting: principles and practice,
2nd edition, OTexts: Melbourne, Australia. OTexts.com/fpp2. Accessed on
<current date> .
This online version of the book was last updated on 6 July 2023.
The print version of the book (available from Amazon and Google) was last
updated on 8 May 2018.
Bibliography
Wickham, H. (2016). ggplot2: Elegant graphics for data analysis (2nd ed). Springer.
[Amazon]