Someone sent me some questions by email, and I decided to answer some of them here. Continue reading →

# Tag / forecasting

# Tourism forecasting competition data as an R package

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.

Thanks to Peter Ellis for putting the package together. He has also produced a nice blog post about it.

# GEFCom2017: Hierarchical Probabilistic Load Forecasting

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.

# Call for forecasting workshops in Cairns, Australia

The 37th annual **International Symposium on Forecasting** will be held in Cairns, Australia, from 25-28 June 2017. We plan to hold some workshops on Sunday 25 June, before the main conference. Continue reading →

# Forecast intervals for aggregates

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.

# R package forecast v7.2 now on CRAN

I’ve pushed a minor update to the forecast package to CRAN. Some highlights are listed here.

# R packages for forecast combinations

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**.

# Sponsorship for the Cairns forecasting conference

Regular readers will know that the International Symposium on Forecasting is coming to Australia in June 2017. This is the leading international forecasting conference, and one I’ve attended every year for the past 17 years.

It will be held in Cairns, Australia — one of the most beautiful locations in the country (and there is some stiff competition!) and right next to the Great Barrier Reef. Some further information is available on our website (still in progress).

This is only the second time it has been held in Australia, with the 2004 conference being held in Sydney. We expect to get about 300 people attending, 2/3 from academia and 1/3 from business, industry and government.

Right now, I’m looking for organizations who wish to get involved with some sponsorship. Sponsor information is highly visible at the conference, as well as on the website, the program and other publications, so it is an opportunity to support the forecasting community, promote your organization, and perhaps recruit some young rising stars in the analytics world. Continue reading →

# The thief package for R: Temporal HIErarchical Forecasting

I have a new R package available to do temporal hierarchical forecasting, based on my paper with George Athanasopoulos, Nikolaos Kourentzes and Fotios Petropoulos. (Guess the odd guy out there!)

It is called “thief” – an acronym for Temporal HIErarchical Forecasting. The idea is to take a seasonal time series, and compute all possible temporal aggregations that result in an integer number of observations per year. For example, a quarterly time series is aggregated to biannual and annual; while a monthly time series is aggregated to 2-monthly, quarterly, 4-monthly, biannual and annual. Each of the resulting time series are forecast, and then the forecasts are reconciled using the hierarchical reconciliation algorithm described in our paper.

It turns out that this tends to give better forecasts, even though no new information has been added, especially for time series with long seasonal periods. It also allows different types of forecasts for different forecast horizons to be combined in a consistent manner.

# “Forecasting with R” short course in Eindhoven

I will be giving my 3-day short-course/workshop on “Forecasting with R” in Eindhoven (Netherlands) from 19-21 October.

Details at https://www.win.tue.nl/~adriemel/shortcourse.html