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.
This poem was written by David Goddard from the Monash University Department of Epidemiology and Preventive Medicine. It is reproduced here with his permission. The poem won the inaugural Monash University poetry competition and will soon be published in an anthology of contemporary poetry. Continue reading →
The International Association for Statistical Computing (IASC) is holding a Data Analysis Competition. Winners will be invited to present their work at the Joint Meeting of IASC-ABE Satellite Conference for the 60th ISI WSC 2015 to be held at Atlântico Búzios Convention & Resort in Búzios, RJ, Brazil (August 2–4, 2015). They will also be invited to submit a manuscript for possible publication (following peer review) to IASC’s official journal, Computational Statistics & Data Analysis. Continue reading →
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 →
I’m delighted that Professor Dianne Cook will be joining Monash University in July 2015 as a Professor of Business Analytics. Di is an Australian who has worked in the US for the past 25 years, mostly at Iowa State University. She is moving back to Australia and joining the Department of Econometrics and Business Statistics in the Monash Business School, as part of our initiative in Business Analytics.
Di is a world leader in data visualization, and is well-known for her work on interactive graphics. She is also the academic supervisor of several leading data scientists including Hadley Wickham and Yihui Xie, both of whom work for RStudio.
Di has a great deal of energy and enthusiasm for computational statistics and data visualization, and will play a key role in developing and teaching our new subjects in business analytics.
The Monash Business School is already exceptionally strong in econometrics (ranked 7th in the world on RePEc), and forecasting (ranked 11th on RePEc), and we have recently expanded into actuarial science. With Di joining the department, we will be extending our expertise in the area of data visualization as well.
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 →