A new version of my
hts package for R is now on CRAN. It was completely re-written from scratch. Not a single line of code survived. There are some minor syntax changes, but the biggest change is speed and scope. This version is many times faster than the previous version and can handle hundreds of thousands of time series without complaining. (more…)
I occasionally get email asking how to detect whether seasonality is present in a data set. Sometimes the period of the potential seasonality is known, but in other cases it is not.
I’ve discussed before how to estimate an unknown seasonal period, and how to measure the strength of the seasonality. In this post, I want to look at testing if a series is seasonal when the potential period is known (e.g., with quarterly, monthly, daily or hourly data). (more…)
We are currently selecting the cover design for OTexts books. The first one to go into print will be Forecasting: principles and practice. We have narrowed the choice to the two designs below, although changes are still possible. I thought it would be useful to get some feedback on these designs from readers of this blog (and from people who subscribe to my twitter feed). (more…)
This is the third interview I’ve done in the last 18 months. The others were for:
Every year or so, Elsevier asks me to nominate five International Journal of Forecasting papers from the last two years to highlight in their marketing materials as “Editor’s Choice”. I try to select papers across a broad range of subjects, and I take into account citations and downloads as well as my own impression of the paper. That tends to bias my selection a little towards older papers as they have had more time to accumulate citations. Here are the papers I chose this morning (in the order they appeared):
- Diebold and Yilmaz (2012) Better to give than to receive: Predictive directional measurement of volatility spillovers. IJF 28(1), 57–66.
- Loterman, Brown, Martens, Mues, and Baesens (2012) Benchmarking regression algorithms for loss given default modeling. IJF 28(1), 161–170.
- Soyer and Hogarth (2012) The illusion of predictability: How regression statistics mislead experts. IJF 28(3), 695–711.
- Friedman (2012) Fast sparse regression and classification. IJF 28(3), 722–738.
- Davydenko and Fildes (2013) Measuring forecasting accuracy: The case of judgmental adjustments to SKU-level demand forecasts. IJF 29(3), 510–522.
Last time I did this, three of the five papers I chose went on to win awards. (I don’t pick the award winners — that’s a matter for the whole editorial board.) On the other hand, I didn’t pick the paper that got the top award for the period 2010–2011. So perhaps my selection is not such a good guide.
I recently co-authored a chapter on “Prospective Life Tables” for this book, edited by Arthur Charpentier. R code to reproduce the figures and to complete the exercises for our chapter is now available on github. Code for the other chapters should also be available soon. The book can be pre-ordered on Amazon.
Dave Giles pointed out on his blog yesterday that my department is currently ranked in the top 10 in the world for econometrics, according to IDEAS. We are also ranked 13th in the world in forecasting. Since IDEAS only covers the economics literature, the forecasting rank does not take account of our work in other areas such as demographic forecasting, and electricity demand forecasting.
These rankings are only a rough indication of quality, but it is nice to see the department being recognized.
In two weeks I am presenting a workshop at the University of Granada (Spain) on Automatic Time Series Forecasting.
Unlike most of my talks, this is not intended to be primarily about my own research. Rather it is to provide a state-of-the-art overview of the topic (at a level suitable for Masters students in Computer Science). I thought I’d provide some historical perspective on the development of automatic time series forecasting, plus give some comments on the current best practices. (more…)
Hastie, Tibshirani and Friedman’s Elements of Statistical Learning first appeared in 2001 and is already a classic. It is my go-to book when I need a quick refresher on a machine learning algorithm. I like it because it is written using the language and perspective of statistics, and provides a very useful entry point into the literature of machine learning which has its own terminology for statistical concepts. A free downloadable pdf version is available on the website.
Recently, a simpler related book appeared entitled Introduction to Statistical Learning with applications in R by James, Witten, Hastie and Tibshirani. It “is aimed for upper level undergraduate students, masters students and Ph.D. students in the non-mathematical sciences”. This would be a great textbook for our new 3rd year subject on Business Analytics. The R code is a welcome addition in showing how to implement the methods. Again, a free downloadable pdf version is available on the website.
There is also a new, free book on Statistical foundations of machine learning by Bontempi and Ben Taieb available on the OTexts platform. This is more of a handbook and is written by two authors coming from a machine learning background. R code is also provided. Being an OTexts book, it is continually updated and revised, and is freely available to anyone with a browser.
Thanks to the authors for being willing to make these books freely available.