An invited session consists of 3 or 4 talks around a specific forecasting theme. You are allowed to be one of the speakers in a session you organize (although it is not necessary). So if you know what you are planning to speak about, all you need to do is find 2 or 3 other speakers who will speak on something related, and invite them to join you. The length of all such invited talks will be about 20 minutes.
Invited sessions will be marked as such on the program and carry a slightly higher status than a contributed session. Unfortunately, we can’t offer any financial support for these invited speakers or session organizers.
If you are interested in organizing an invited session, please contact us with your topic. The deadline for proposals is 28 February 2017. We don’t need to know who will speak at it — you have a few months to find willing participants after you agree to organize a session.
The ISF is a little different from most academic conferences in that about 1/3 of the attendees are practitioners, and 2/3 are academics. Consequently, we are not only interested in traditional academic sessions, but also in talks from company-based forecasters describing the forecasting challenges they face, and hopefully some of the solutions.
See forecasters.org/isf/ for more information about the conference, and the location. Cairns is one of the most beautiful places in Australia, and very close to the Great Barrier Reef. June is also the best time to visit the area, as it is during the dry season with moderate temperatures and lots of sunshine. We are hoping that people attending the conference will choose to have a holiday in the region as well.
A major news outlet interviewed me on predictive analytics. Here were my responses. Continue reading →
Someone sent me some questions by email, and I decided to answer some of them here. Continue reading →
The data used in the tourism forecasting competition, discussed in Athanasopoulos et al (2011), have been made available in the Tcomp package for R. The objects are of the same format as for Mcomp package containing data from the M1 and M3 competitions.
After the great success of the previous two energy forecasting competitions we have run (GEFCom2012 and GEFCom2014), we are holding another one, this time focused on hierarchical probabilistic load forecasting. Check out all the details over on Tao Hong’s blog.
The previous GEFComs have led to some major advances in forecasting methodology, available via IJF papers by the winning teams. I expect similar developments to arise out of this competition. Winners get to present their work in Cairns, Australia at ISEA2017.
A common problem is to forecast the aggregate of several time periods of data, using a model fitted to the disaggregated data. For example, you may have monthly data but wish to forecast the total for the next year. Or you may have weekly data, and want to forecast the total for the next four weeks.
If the point forecasts are means, then adding them up will give a good estimate of the total. But prediction intervals are more tricky due to the correlations between forecast errors.
I’ve pushed a minor update to the forecast package to CRAN. Some highlights are listed here.
It has been well-known since at least 1969, when Bates and Granger wrote their famous paper on “The Combination of Forecasts”, that combining forecasts often leads to better forecast accuracy.
So it is helpful to have a couple of new R packages which do just that: opera and forecastHybrid.