# Jobs at Monash University

We have two new continuing positions currently being advertised in our department: for lecturer and senior lecturer. Details are on the Monash website. (For those in North America, a lecturer is equivalent to your assistant professor, and a senior lecturer is equivalent to your associate professor. See the Wikipedia article on Australian academic ranks for more information.)

Although the title says “Lecturer/Senior Lecturer (Econometrics)”, we are interested in a wider range of candidates including statistics and machine learning. I’d particularly like to see strong candidates in computational statistics and machine learning, to add to our growing strength in this area.

Applications close on 20 January 2016. Please direct enquiries to Professor Farshid Vahid.

# Piecewise linear trends

I prepared the following notes for a consulting client, and I thought they might be of interest to some other people too.

Let $y_t$ denote the value of the time series at time $t$, and suppose we wish to fit a trend with correlated errors of the form
$$y_t = f(t) + n_t,$$
where $f(t)$ represents the possibly nonlinear trend and $n_t$ is an autocorrelated error process. Continue reading →

# forecast package v6.2

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 →

# Stanford seminar

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 →

# Reproducibility in computational research

Jane Frazier spoke at our research team meeting today on “Reproducibility in computational research”. We had a very stimulating and lively discussion about the issues involved. One interesting idea was that reproducibility is on a scale, and we can all aim to move further along the scale towards making our own research more reproducible. For example

• Can you reproduce your results tomorrow on the same computer with the same software installed?
• Could someone else on a different computer reproduce your results with the same software installed?
• Could you reproduce your results in 3 years time after some of your software environment may have changed?
• etc.

Think about what changes you need to make to move one step further along the reproducibility continuum, and do it.

Jane’s slides and handout are below. Continue reading →

# Chinese R conference

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.

# Upcoming talks in California

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.

# International Symposium on Forecasting: Spain 2016

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.

### Speakers:

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

### Important Dates

Invited Session Proposals: January 31 2016
Abstract Submissions: March 16 2016
Early Registration Ends: May 15 2016