Saw this in r/statistics and thought it was pretty interesting. is a pretty well regarded time series that has previously been very stats oriented. The github for the models is here:
I have taken u/true_believer ‘s post below:

  • The combination of methods was the king of the M4. Of the 17 most accurate methods, 12 were “combinations” of mostly statistical approaches.

  • The biggest surprise was a “hybrid” approach that utilised both statistical and ML features. This method produced both the most accurate forecasts and the most precise PIs, and was submitted by Slawek Smyl, a Scientist at Uber Technologies. According to sMAPE, it was close to 10% more accurate than the combination benchmark.

  • The second most accurate method was a combination of seven statistical methods and an ML one, with the weights for the averaging calculated by an ML algorithm that was trained to minimise the forecasting error through holdout tests. This method was submitted jointly by Spain’s University of A Coruña and Australia’s Monash University.

  • The most accurate and second most accurate methods also achieved an amazing in specifying the 95% PIs correctly. These are the first methods we are aware of that have done so, rather than underestimating the uncertainty considerably.

  • The six pure ML methods that were submitted in the M4 all performed poorly, with none of them being more accurate than Comb and only one being more accurate than Naïve2. This supports the findings of the latest PLOS ONE paper by Makridakis, Spiliotis and Assimakopoulos.

Edit: A paper with preliminary results is now available at

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thanks you RSS link


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