### Hyndsight

Thoughts on research, forecasting, statistics, and other distractions.

# All Hyndsight posts by date

## 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.

## 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).

## 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.

## Data Science for Managers: May 2018

For the last few years, I have been involved with running a 3-day short course on “Data Science for Managers”. We have run it twice each year since 2015, and it continues to prove very popular. We have some awesome presenters including Monash University professors Di Cook, Geoff Webb, and Kim Marriott, as well as several very experienced data scientists working in industry. The next course will be held on 8-10 May 2018.

## Some new time series packages

This week I have finished preliminary versions of two new R packages for time series analysis. The first (tscompdata) contains several large collections of time series that have been used in forecasting competitions; the second (tsfeatures) is designed to compute features from univariate time series data. For now, both are only on github. I will probably submit them to CRAN after they’ve been tested by a few more people.

## 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.