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.

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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.

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Tourism time series repository

A few years ago, I wrote a paper with George Athanasopoulos and others about a tourism forecasting competition. We originally made the data available as an online supplement to the paper, but that has unfortunately since disappeared although the paper itself is still available.

So I am posting the data here in case anyone wants to use it for replicating our results, or for other research purposes. The data are split into monthly, quarterly and yearly data. There are 366 monthly series, 427 quarterly series and 518 yearly series. Each group of series is further split into training data and test data. Further information is provided in the paper.

If you use the data in a publication, please cite the IJF paper as the source, along with a link to this blog post.

Download the zip file

The latest IJF issue with GEFCom2014 results

The latest issue of the IJF is a bumper issue with over 500 pages of forecasting insights.

The GEFCom2014 papers are included in a special section on probabilistic energy forecasting, guest edited by Tao Hong and Pierre Pinson. This is a major milestone in energy forecasting research with the focus on probabilistic forecasting and forecast evaluation done using a quantile scoring method. Only a few years ago I was having to explain to energy professionals why you couldn’t use a MAPE to evaluate a percentile forecast. With this special section, we now have a tutorial review on probabilistic electric load forecasting by Tao Hong and Shu Fan, which should help everyone get up to speed with current forecasting approaches, evaluation methods and common misunderstandings. The section also contains a large number of very high quality articles showing how to do state-of-the-art density forecasting for electricity load, electricity price, solar and wind power. Moreover, we have some benchmarks on publicly available data sets so future researchers can easily compare their methods against these published results.

In addition to the special section on probabilistic energy forecasting, there is an invited review paper on call centre forecasting by Ibrahim, Ye, L’Ecuyer and Shen. This is an important area in practice, and this paper provides a helpful review of the literature, a summary of the key issues in building good models, and suggests some possible future research directions.

There is also an invited paper from Blasques, Koopman, Łasak and Lucas on “In-sample confidence bands and out-of-sample forecast bands for time-varying parameters in observation-driven models” with some great discussion from Catherine Forbes and Pierre Perron. This was the subject of Siem Jan Koopman’s ISF talk in 2014.

Finally, there are 18 regular contributed papers, more than we normally publish in a whole issue, on topics ranging from forecasting excess stock returns to demographics of households, from forecasting food prices, to evaluating forecasts of counts and intermittent demand. Check them all out on ScienceDirect.

2017 International Symposium on Energy Analytics

Predictive Energy Analytics in the Big Data World

Cairns, Australia, June 22-23, 2017



This will be a great conference, and it is in a great location — Cairns, Australia, right by the Great Barrier Reef. Even better, if you stay on you can attend the International Symposium on Forecasting which immediately follows the International Symposium on Energy Analytics.

So block out 22-28 June 2017 on your calendars so you can enjoy a tropical paradise in one of the most beautiful parts of Australia, while attending two awesome conferences.

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