I keep telling students that there are lots of jobs in data science (including statistics), and they often tell me they can’t find them advertised. As usual, you do have to do some networking, and one of the best ways of doing it is via a Data Science Meetup. Many cities now have them including Melbourne, Sydney, London, etc. It is the perfect opportunity to meet with local employers, many of which are hiring due to the huge expansion in the use of data analysis in business (aka business analytics).
At the end of each Melbourne meetup, some employers have been advertising their current analytic job openings to the audience.
Now the local organizers are going to extend the opportunity to allow job-searchers to give a 90 second pitch to employers. Details are provided on the message board.
The International Institute of Forecasters sponsors workshops every year, each of which focuses on a specific theme. The purpose of these workshops is to facilitate small, informal meetings where experts in a particular field of forecasting can discuss forecasting problems, research, and solutions. Over the years, our workshops have covered topics from Predicting Rare Events, ICT Forecasting, and, most recently, Singular Spectrum Analysis. Often these workshops are associated with a special issue of the International Journal of Forecasting.
If you are already hosting a workshop on a forecasting topic and need support from the IIF, or if you are interested in organising and hosting a new workshop, please contact George Athanasopoulos.
A list of past workshops and workshop guidelines are provided on the IIF website.
I’ve received a few emails about including regression variables (i.e., covariates) in TBATS models. As TBATS models are related to ETS models,
tbats() is unlikely to ever include covariates as explained here. It won’t actually complain if you include an
xreg argument, but it will ignore it.
When I want to include covariates in a time series model, I tend to use
auto.arima() with covariates included via the
xreg argument. If the time series has multiple seasonal periods, I use Fourier terms as additional covariates. See my post on forecasting daily data for some discussion of this model. Note that
fourierf() now handle
msts objects, so it is very simple to do this.
For example, if
holiday contains some dummy variables associated with public holidays and
holidayf contains the corresponding variables for the first 100 forecast periods, then the following code can be used:
y <- msts(x, seasonal.periods=c(7,365.25))
z <- fourier(y, K=c(5,5))
zf <- fourierf(y, K=c(5,5), h=100)
fit <- auto.arima(y, xreg=cbind(z,holiday), seasonal=FALSE)
fc <- forecast(fit, xreg=cbind(zf,holidayf), h=100)
The main disadvantage of the ARIMA approach is that the seasonality is forced to be periodic, whereas a TBATS model allows for dynamic seasonality.
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.
Today I read a paper that had been submitted to the IJF which included the following figure
along with several similar plots. (Click for a larger version.) I haven’t seen anything this bad for a long time. In fact, I think I would find it very difficult to reproduce using R, or even Excel (which is particularly adept at bad graphics).
A few years ago I produced “Twenty rules for good graphics”. I think I need to add a couple of additional rules:
- Represent time changes using lines.
- Never use fill patterns such as cross-hatching.
(My original rule #20 said Avoid pie charts.)
It would have been relatively simple to show these data as six lines on a plot of GDP against time. That would have made it obvious that the European GDP was shrinking, the GDP of Asia/Oceania was increasing, while other regions of the world were fairly stable. At least I think that is what is happening, but it is very hard to tell from such graphical obfuscation.
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
I am now using biblatex for all my bibliographic work as it seems to have developed enough to be stable and reliable. The big advantage of biblatex is that it is easy to format the bibliography to conform to specific journal or publisher styles. It is also possible to have structured bibliographies (e.g., divided into sections: books, papers, R packages, etc.) Continue reading →