I’m speaking in the “Yahoo Labs Big Thinkers” series on Friday 26 June. I hope I can live up to the title!
My talk is on “Exploring the boundaries of predictability: what can we forecast, and when should we give up?” Essentially I will start with some of the ideas in this post, and then discuss the features of hard-to-forecast time series.
So if you’re in the San Francisco Bay area, please come along. Otherwise, it will be streamed live on the Yahoo Labs website. Continue reading →
It is now exactly 12 months since the print version of my forecasting textbook with George Athanasopoulos was released on Amazon.com. Although the book is freely available online, it seems that a lot of people still like to buy print books. Continue reading →
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 →
I’m currently attending the one day workshop on this topic at QUT in Brisbane. This morning I spoke on “Visualizing and forecasting big time series data”. My slides are here.
The talks are being streamed.
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’ve now resurrected the collection of research journals that I follow, and set it up as a shared collection in feedly. So anyone can easily subscribe to all of the same journals, or select a subset of them, to follow on feedly. 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 →
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.
Naturally, we are not able to provide free technical support, although we welcome bug reports. We are available to undertake paid consulting work in electricity forecasting.
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 →