Antoine Ledoux, Quantitative Research Analyst
December 7, 2022
These days new Machine Learning techniques seem to be coming out every other month claiming to be the next big thing and it is hard to distinguish the good from the bad. So, what better way to compare these models than to have them compete against one another to see which one is indeed the best in time series forecasting (our bread and butter).
This is how the M competitions came about. Brought to life in 1982 by Spyros Makridakis (who gave his name to the event, the Makridakis Competitions, now known as M Competitions), the goal of these competitions is to compare all-time series forecasting methods known to date and see which one performs best. The main conclusion from the first M Competitions (hosted in 1982, 1993 and 2000) was that “statistically sophisticated or complex methods do not necessarily provide more accurate forecasts than simpler ones”.
However, this changed somewhat in the 4th competition (M4) when a data scientist from Uber Technologies surprised the jury with a hybrid approach utilizing both Statistical and Machine Learning methods, beating the benchmark by a whopping 10%. For the first time, the best approaches were hybrid ones: classical statistical models like EWMA or ARIMA were used to create features that were then fed into a more complex Machine Learning model. In M5, the top performers were pure Machine Learning models, beating all statistical benchmarks and their combinations. The latest competition (M6) is still on-going and relevant to our work at Graham, as it will compare forecasting models on price data for 50 S&P500 stocks and 50 international ETFs. May the best forecasting model win!
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