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 naï...

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 pack...

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...

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 ...

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 ...

I’ve caved in to the hex sticker craze, and produced some hex stickers for the forecast package for R. If you attend a workshop I teach, I’ll give you one.

For the past few months I’ve been working on a new DataCamp course teaching Forecasting using R. I’m delighted that it is now available for anyone to do. Course blurb Forecasting involves making predictions about the future. It is required in man...

The nnetar function in the forecast package for R fits a neural network model to a time series with lagged values of the time series as inputs (and possibly some other exogenous inputs). So it is a nonlinear autogressive model, and it is not possible to analytically derive prediction intervals. ...