It is common to want forecasts to be positive, or to require them to be within some specified range . Both of these situations are relatively easy to handle using transformations. (more…)
Sometimes it is useful to “backcast” a time series — that is, forecast in reverse time. Although there are no in-built R functions to do this, it is very easy to implement. Suppose
x is our time series and we want to backcast for periods. Here is some code that should work for most univariate time series. The example is non-seasonal, but the code will also work with seasonal data. (more…)
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.