My 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.
A few weeks ago I had a Skype chat with Tim Harford, the “Undercover Economist” for Britain’s Financial Times. He was working on an article for the FT on forecasting, and wanted my perspective as an academic forecaster. I mostly talked about what makes some things more predictable than others, as discussed in this blog post. In the end, his article headed in a different direction, so I don’t get quoted, but it is still a good read!
He also put out this YouTube summary, for those who don’t like to read:
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.
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
GEFCom 2014 is the most advanced energy forecasting competition ever organized, both in terms of the data involved, and in terms of the way the forecasts will be evaluated.
So everyone interested in energy forecasting should head over to the competition webpage and start forecasting: www.gefcom.org.
This time, the competition is hosted on CrowdANALYTIX rather than Kaggle.
Highlights of GEFCom2014:
- An upgraded edition from GEFCom2012
- Four tracks: electric load, electricity price, wind power and solar power forecasting.
- Probabilistic forecasting: contestants are required to submit 99 quantiles for each step throughout the forecast horizon.
- Rolling forecasting: incremental data sets are being released on weekly basis to forecast the next period of interest.
- Prizes for winning teams and institutions: up to 3 teams from each track will be recognized as the winning team; top institutions with multiple well-performing teams will be recognized as the winning institutions.
- Global participation: 200+ people from 40+ countries have already signed up the GEFCom2014 interest list.
Tao Hong (the main organizer) has a few tips on his blog that you should read before starting.
At the IIF annual board meeting last month in Rotterdam, I suggested that we provide awards to the top students studying forecasting at university level around the world, to the tune of $100 plus IIF membership for a year. I’m delighted that the idea met with enthusiasm, and that the awards are now available. Even better, my second year forecasting subject has been approved for an award.
The IIF have agreed to fund awards for 20 forecasting courses to start with. I believe they have already had several applications, so any other forecasting lecturers out there will need to be quick if they want to be part of it.
This is an example of how to use the demography package in R for stochastic population forecasting with coherent components. It is based on the papers by Hyndman and Booth (IJF 2008) and Hyndman, Booth and Yasmeen (Demography 2013). I will use Australian data from 1950 to 2009 and forecast the next 50 years.
In demography, “coherent” forecasts are where male and females (or other sub-groups) do not diverge over time. (Essentially, we require the difference between the groups to be stationary.) When we wrote the 2008 paper, we did not know how to constrain the forecasts to be coherent in a functional data context and so this was not discussed. My later 2013 paper provided a way of imposing coherence. This blog post shows how to implement both ideas using R. Continue reading →
When modelling data with ARIMA models, it is sometimes useful to plot the inverse characteristic roots. The following functions will compute and plot the inverse roots for any fitted ARIMA model (including seasonal models). Continue reading →
Rolling forecasts are commonly used to compare time series models. Here are a few of the ways they can be computed using R. I will use ARIMA models as a vehicle of illustration, but the code can easily be adapted to other univariate time series models. Continue reading →