Follow-up forecasting forum in Eindhoven

Last October I gave a 3-day masterclass on “Forecasting with R” in Eindhoven, Netherlands. There is a follow-up event planned for Tuesday 18 April 2017. It is particularly designed for people who attended the 3-day class, but if anyone else wants to attend they would be welcome.

Please register here if you want to attend. Continue reading →

Come to Melbourne, even if not to Monash

The University of Melbourne is advertising for a “Professor in Statistics (Data Science)”. Melbourne (the city) is fast becoming a vibrant centre for data science and applied statistics, with more than 4700 people signed up for the Data Science Meetup Group, a thriving start-up scene, the group at Monash Business School (including Di Cook and me), and the Monash Centre for Data Science (including Geoff Webb and Wray Buntine). Not to mention that Melbourne is a wonderful place to live, having won the “World’s most liveable city” award from the Economist for the last 6 years in a row.

Actually, the Uni of Melbourne currently has two professorships on offer — the other being the Peter Hall Chair in Mathematical Statistics. (Not sure that anyone would actually feel qualified to have a job with that title!)

So any professors of statistics out there looking for a new challenge, please consider coming to Melbourne. We’ll even invite you to visit us from time to time at Monash.

 

Statistics positions available at Monash University

We are hiring again, and looking for people in statistics, econometrics and related fields (such as actuarial science, machine learning, and business analytics). We have a strong business analytics group (with particular expertise in data visualization, machine learning, statistical computing, R, and forecasting), and it would be great to see it grow. The official advert follows.

Continue reading →

Model variance for ARIMA models

From today’s email:

I wanted to ask you about your R forecast package, in particular the Arima() function. We are using this function to fit an ARIMAX model and produce model estimates and standard errors, which in turn can be used to get p-values and later model forecasts. To double check our work, we are also fitting the same model in SAS using PROC ARIMA and comparing model coefficients and output. Continue reading →