I was recently interviewed as part of a promotion for the Monash Business School. The interviews can be watched below if anyone is interested. The titles chosen weren’t my ideas. Continue reading →
Every week I reject some papers submitted to the International Journal of Forecasting, without sending the papers off to associate editors or reviewers. Here are five of the most common reasons for rejection. Continue reading →
Every two years, the International Journal of Forecasting awards a prize for the best paper published in a two year period. It is now time to identify the best paper published in the IJF during 2012 and 2013. There is always about 18 months delay after the publication period to allow time for reflection, citations, etc. The prize is US$1000 plus an engraved plaque. Continue reading →
Big data is now endemic in business, industry, government, environmental management, medical science, social research and so on. One of the commensurate challenges is how to effectively model and analyse these data.
This workshop will bring together national and international experts in statistical modelling and analysis of big data, to share their experiences, approaches and opinions about future directions in this field.
I’m currently visiting Taiwan and I’m giving two seminars while I’m here — one at the National Tsing Hua University in Hsinchu, and the other at Academia Sinica in Taipei. Details are below for those who might be nearby. Continue reading →
Shu Fan and I have developed a model for electricity demand forecasting that is now widely used in Australia for long-term forecasting of peak electricity demand. It has become known as the “Monash Electricity Forecasting Model”. We have decided to release an R package that implements our model so that other people can easily use it. The package is called “MEFM” and is available on github. We will probably also put in on CRAN eventually.
The model was first described in Hyndman and Fan (2010). We are continually improving it, and the latest version is decribed in the model documentation which will be updated from time to time.
The package is being released under a GPL licence, so anyone can use it. All we ask is that our work is properly cited.
Amongst today’s email was one from someone running a private competition to classify time series. Here are the essential details.
The data are measurements from a medical diagnostic machine which takes 1 measurement every second, and after 32–1000 seconds, the time series must be classified into one of two classes. Some pre-classified training data is provided. It is not necessary to classify all the test data, but you do need to have relatively high accuracy on what is classified. So you could find a subset of more easily classifiable test time series, and leave the rest of the test data unclassified. Continue reading →
The first issue of the IJF for 2015 has just been published, and I’m delighted that it includes a special section honoring Herman Stekler. It includes articles covering a range of his forecasting interests, although not all of them (sports forecasting is missing). Herman himself wrote a paper for it looking at “Forecasting—Yesterday, Today and Tomorrow”.
He is in a unique position to write such a paper as he has been doing forecasting research longer than anyone else on the planet — his first published paper on forecasting appeared in 1959. Herman is now 82 years old, and is still very active in research. Only a couple of months ago, he wrote to me with some new research ideas he had been thinking about, asking me for some feedback. He is also an extraordinarily conscientious and careful associate editor of the IJF and a delight to work with. He is truly “a scholar and a gentleman” and I am very happy that we can honor Herman in this manner. Thanks to Tara Sinclair, Prakash Loungani and Fred Joutz for putting this tribute together.
We also published an interview with Herman in the IJF in 2010 which contains some information about his early years, graduate education and first academic jobs.
Competitions have a long history in forecasting and prediction, and have been instrumental in forcing research attention on methods that work well in practice. In the forecasting community, the M competition and M3 competition have been particularly influential. The data mining community have the annual KDD cup which has generated attention on a wide range of prediction problems and associated methods. Recent KDD cups are hosted on kaggle.
In my research group meeting today, we discussed our (limited) experiences in competing in some Kaggle competitions, and we reviewed the following two papers which describe two prediction competitions:
- Athanasopoulos and Hyndman (IJF 2011). The value of feedback in forecasting competitions. [preprint version]
- Roy et al (2013). The Microsoft Academic Search Dataset and KDD Cup 2013.