Predicting/hypothesizing the findings of the M5 competition

The scientific method consists of making hypotheses or predictions and then carrying out experiments to test them once the actual results have become available, in order to learn from both successes and mistakes. This approach was followed in the M4 competition with positive results and has been repeated in the M5, with its organizers submitting their ten predictions/hypotheses about its expected results five days before its launch. The present paper presents these predictions/hypotheses and evaluates their realization according to the actual findings of the competition. The results indicate that well-established practices, like combining forecasts, exploiting explanatory variables, and capturing seasonality and special days, remain critical for enhancing forecasting performance, re-confirming also that relatively new approaches, like cross-learning algorithms and machine learning methods, display great potential. Yet, we show that simple, local statistical methods may still be competitive for forecasting high granularity data and estimating the tails of the uncertainty distribution, thus motivating future research in the field of retail sales forecasting.