FPP now available as a downloadable e-book

FPP coverMy forecasting textbook with George Athanasopoulos is already available online (for free), and in print via Amazon (for under $40). Now we have made it available as a downloadable e-book via Google Books (for $15.55). The Google Books version is identical to the print version on Amazon (apart from a few typos that have been fixed).

To use the e-book version on an iPad or Android tablet, you need to have the Google Books app installed [iPad, Android]. You could also put it on an iPhone or Android phone, but I wouldn’t recommend it as the text will be too small to read.

You can download a free sample (up to the end of Chapter 2) if you want to check how it will look on your device.

The sales of the print and e-book versions are used to fund the running the OTexts website where all OTexts books are freely available.

The online version is continuously updated — any errors discovered are fixed immediately. The print and e-book versions will be updated approximately annually to bring them into line with the online version.


Resources for the FPP book

The FPP resources page has recently been updated with several new additions including

  • R code for all examples in the book. This was already available within each chapter, but the examples have been collected into one file per chapter to save copying and pasting the various code fragments.
  • Slides from a course on Predictive Analytics from the University of Sydney.
  • Slides from a course on Economic Forecasting from the University of Hawaii.

If any one using the book has other material that could be made available, please send them to me. For example, recorded lectures, slides, additional examples, assignments, exam questions, solutions, etc.

Forecasting with R in WA

On 23-25 September, I will be running a 3-day workshop in Perth on “Forecasting: principles and practice” mostly based on my book of the same name.

Workshop participants will be assumed to be familiar with basic statistical tools such as multiple regression, but no knowledge of time series or forecasting will be assumed. Some prior experience in R is highly desirable.

Venue: The University Club, University of Western Australia, Nedlands WA.

Day 1:
Forecasting tools, seasonality and trends, exponential smoothing.
Day 2:
State space models, stationarity, transformations, differencing, ARIMA models.
Day 3:
Time series cross-validation, dynamic regression, hierarchical forecasting, nonlinear models.

The course will involve a mixture of lectures and practical sessions using R. Each participant must bring their own laptop with R installed, along with the fpp package and its dependencies.

For costs and enrolment details, go to

My forecasting book now on Amazon

For all those people asking me how to obtain a print version of my book “Forecasting: principles and practice” with George Athanasopoulos, you now can.

FPP cover

Order on Amazon.com

Order on Amazon.co.uk

Order on Amazon.fr

The online book will continue to be freely available. The print version of the book is intended to help fund the development of the OTexts platform.

The price is US$45, £27 or €35.

Compare that to $195 for my previous forecasting textbook, $150 for Fildes and Ord, or $182 for Gonzalez-Rivera. No matter how good the books are, the prices are absurdly high.

OTexts is intended to be a different kind of publisher — all our books are online and free, those in print will be reasonably priced.

The online version will continue to be updated regularly. The print version is a snapshot of the online version today. We will release a new print edition occasionally, no more than annually and only when the online version has changed enough to warrant a new print edition.

We are planning an offline electronic version as well. I’ll announce it here when it is ready.

Fast computation of cross-validation in linear models

The leave-one-out cross-validation statistic is given by
\text{CV} = \frac{1}{N} \sum_{i=1}^N e_{[i]}^2,

where ${e_{[i]} = y_{i} – \hat{y}_{[i]}} $, the observations are given by $y_{1},\dots,y_{N}$, and $\hat{y}_{[i]}$ is the predicted value obtained when the model is estimated with the $i\text{th}$ case deleted. This is also sometimes known as the PRESS (Prediction Residual Sum of Squares) statistic.

It turns out that for linear models, we do not actually have to estimate the model $N$ times, once for each omitted case. Instead, CV can be computed after estimating the model once on the complete data set. Continue reading →

Highlighting the web

Users of my new online forecasting book have asked about having a facility for personal highlighting of selected sections, as students often do with print books. We have plans to make this a built-in part of the platform, but for now it is possible to do it using a simple browser extension. This approach allows any website to be highlighted, so is even more useful than if we only had the facility on OTexts.org.

There are several possible tools available. One of the simplest tools that allows both highlighting and annotations is Diigo. Continue reading →