Subject ▸ Forecasting

A forecast ensemble benchmark

Forecasting benchmarks are very important when testing new forecasting methods, to see how well they perform against some simple alternatives. Every week I get sent papers proposing new forecasting methods that fail to do better than even the simplest benchmark. They are rejected without review. Typical benchmarks include the naïve method (especially for finance and economic data), the seasonal naïve method (for seasonal data), an automatically selected ETS model, and an automatically selected ARIMA model.

Read More…

Forecasting in NYC: 25-27 June 2018

In late June, I will be in New York to teach my 3-day workshop on Forecasting using R. Tickets are available at Eventbrite. This is the first time I’ve taught this workshop in the US, having previously run it in the Netherlands and Australia. It will be based on the 2nd edition of my book “Forecasting: Principles and Practice” with George Athanasopoulos. All participants will get a print version of the book.

Read More…

Upcoming talks: May-July 2018

First semester teaching is nearly finished, and that means conference season for me. Here are some talks I’m giving in the next two months. Click the links for more details. Melbourne, Australia. 28 May: Panel discussion: Forecasting models, the uncertainties and associated risk Boulder, Colorado, USA. 17-20 June: International Symposium on Forecasting. I’ll be talking about “Tidy forecasting in R”. New York, USA. 21 June: Feature-based time series analysis. New York Open Statistical Programming Meetup, eBay NYC.

Read More…

forecast v8.3 now on CRAN

The latest version of the forecast package for R is now on CRAN. This is the version used in the 2nd edition of my forecasting textbook with George Athanasopoulos. So readers should now be able to replicate all examples in the book using only CRAN packages. A few new features of the forecast package may be of interest. A more complete Changelog is also available. mstl() handles multiple seasonality STL decomposition was designed to handle a single type of seasonality, but modern data often involves several seasonal periods (e.

Read More…

A brief history of time series forecasting competitions

Prediction competitions are now so widespread that it is often forgotten how controversial they were when first held, and how influential they have been over the years. To keep this exercise manageable, I will restrict attention to time series forecasting competitions — where only the history of the data is available when producing forecasts. Nottingham studies The earliest non-trivial study of time series forecast accuracy was probably by David Reid as part of his PhD at the University of Nottingham (1969).

Read More…

R package for M4 Forecasting Competition

The M4 forecasting competition is well under-way, and a few of my PhD students have been working on submissions. Pablo Montero-Manso, Carla Netto, and Thiyanga Talagala have made an R package containing all of the data (100,000 time series), which should make it substantially easier for other contestants to load the data into R in order to compute forecasts. Grab the package from this github repository. For more details about the M4 competition see this post or go to the M4 website.

Read More…

IJF Tao Hong Award 2018

Every two years, the International Journal of Forecasting awards a prize to the best paper on energy forecasting. The prize is generously funded by Professor Tao Hong. This year, we will award the prize to a paper published in the IJF during the period 2015-2016. The prize will be US$1000 plus an engraved plaque. The award committee is Rob J Hyndman, Pierre Pinson and James Mitchell. Nominations are invited from any reader of the IJF.

Read More…

M4 Forecasting Competition update

The official guidelines for the M4 competition have now been published, and there have been several developments since my last post on this. There is now a prize for prediction interval accuracy using a scaled version of the Mean Interval Score. If the $100(1-\alpha)$% prediction interval for time $t$ is given by $[L_{t},U_{t}]$, for $t=1,\dots,h$, then the MIS is defined as $$\frac{1}{h}\sum_{t=1}^{h} \left[ (U_t-L_t) + \frac{2}{\alpha}(L_t-Y_t)1(Y_t < L_t) + \frac{2}{\alpha}(Y_t-U_t)1(Y_t > U_t) \right] $$ where $Y_t$ is the observation at time $t$.

Read More…

M4 Forecasting Competition: response from Spyros Makridakis

Following my post on the M4 competition yesterday, Spyros Makridakis sent me these comments for posting here. I would like to thank Rob, my friend and co-author, for his insightful remarks concerning the upcoming M4 competition. As Rob says, the two of us have talked a great deal about competitions and I certainly agree with him about the “ideal” forecasting competition. In this reply, I will explain why I have deviated from the “ideal”, mostly for practical reasons and to ensure higher participation.

Read More…

M4 Forecasting Competition

The “M” competitions organized by Spyros Makridakis have had an enormous influence on the field of forecasting. They focused attention on what models produced good forecasts, rather than on the mathematical properties of those models. For that, Spyros deserves congratulations for changing the landscape of forecasting research through this series of competitions. Makridakis & Hibon, (JRSSA 1979) was the first serious attempt at a large empirical evaluation of forecast methods.

Read More…