This is a new competition being organized by EuroStat. The first phase involves nowcasting economic indicators at national and European level including unemployment, HICP, Tourism and Retail Trade and some of their variants.
The main goal of the competition is to discover promising methodologies and data sources that could, now or in the future, be used to improve the production of official statistics in the European Statistical System.
The organizers seem to have been encouraged by the success of Kaggle and other data science competition platforms. Unfortunately, they have chosen not to give any prizes other than an invitation to give a conference presentation or poster, which hardly seems likely to attract many good participants.
The deadline for registration is 10 January 2016. The duration of the competition is roughly a year (including about a month for evaluation).
See the call for participation for more information.
I prepared the following notes for a consulting client, and I thought they might be of interest to some other people too.
Let denote the value of the time series at time , and suppose we wish to fit a trend with correlated errors of the form
where represents the possibly nonlinear trend and is an autocorrelated error process. Continue reading →
It is a while since I last updated the CRAN version of the forecast package, so I uploaded the latest version (6.2) today. The github version remains the most up-to-date version and is already two commits ahead of the CRAN version.
This update is mostly bug fixes and additional error traps. The full ChangeLog is listed below. Continue reading →
I gave a seminar at Stanford today. Slides are below. It was definitely the most intimidating audience I’ve faced, with Jerome Friedman, Trevor Hastie, Brad Efron, Persi Diaconis, Susan Holmes, David Donoho and John Chambers all present (and probably other famous names I’ve missed).
I’ll be giving essentially the same talk at UC Davis on Thursday. Continue reading →
I will be speaking at the Chinese R conference in Nanchang, to be held on 24-25 October, on “Forecasting Big Time Series Data using R”.
Details (for those who can read Chinese) are at china-r.org.
I’m back in California for the next couple of weeks, and will give the following talk at Stanford and UC-Davis.
Optimal forecast reconciliation for big time series data
Time series can often be naturally disaggregated in a hierarchical or grouped structure. For example, a manufacturing company can disaggregate total demand for their products by country of sale, retail outlet, product type, package size, and so on. As a result, there can be millions of individual time series to forecast at the most disaggregated level, plus additional series to forecast at higher levels of aggregation.
A common constraint is that the disaggregated forecasts need to add up to the forecasts of the aggregated data. This is known as forecast reconciliation. I will show that the optimal reconciliation method involves fitting an ill-conditioned linear regression model where the design matrix has one column for each of the series at the most disaggregated level. For problems involving huge numbers of series, the model is impossible to estimate using standard regression algorithms. I will also discuss some fast algorithms for implementing this model that make it practicable for implementing in business contexts.
Stanford: 4.30pm, Tuesday 6th October.
UCDavis: 4:10pm, Thursday 8th October.
June 19-22, 2016
Santander, Spain – Palace of La Magdalena
The International Symposium on Forecasting (ISF) is the premier forecasting conference, attracting the world’s leading forecasting researchers, practitioners, and students. Through a combination of keynote speaker presentations, academic sessions, workshops, and social programs, the ISF provides many excellent opportunities for networking, learning, and fun.
Greg Allenby, The Ohio State University, USA
Todd Clark, Federal Reserve Bank of Cleveland, USA
José Duato, Polytechnic University of Valencia, Spain
Robert Fildes, Lancaster University, United Kingdom
Edward Leamer, UCLA Anderson, USA
Henrik Madsen, Technical University of Denmark
Adrian Raftery, University of Washington, USA
Invited Session Proposals: January 31 2016
Abstract Submissions: March 16 2016
Early Registration Ends: May 15 2016
More information at www.forecasters.org/isf
The last issue of the International Journal of Forecasting for 2015 has been released. This one contains the usual mix of topics, plus a special section on Forecasting in telecommunications and ICT including a nice review article by Nigel Meade and Towhidul Islam. Enjoy!
At the recent International Symposium on Forecasting, held in Riverside, California, Tillman Gneiting gave a great talk on “Evaluating forecasts: why proper scoring rules and consistent scoring functions matter”. It will be the subject of an IJF invited paper in due course.
One of the things he talked about was the “Murphy diagram” for comparing forecasts, as proposed in Ehm et al (2015). Here’s how it works for comparing mean forecasts. Continue reading →
Last week I gave a talk in the Yahoo! Big Thinkers series. The video of the talk is now online and embedded below.