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"Statistical and Machine Learning forecasting methods: Concerns and ways forward"

From the abstract:

After comparing the post-sample accuracy of popular ML [machine learning] methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods.

From the body:

A major innovation that has distinguished forecasting from other fields has been the good number of empirical studies aimed at both the academic community as well as the practitioners interested in utilizing the most accurate methods for their various applications and reducing cost or maximizing benefits by doing so. These studies contributed to establishing two major changes in the attitudes towards forecasting: First, it was established that methods or models, that best fitted available data, did not necessarily result in more accurate post sample predictions (a common belief until then). Second, the post-sample predictions of simple statistical methods were found to be at least as accurate as the sophisticated ones. This finding was furiously objected to by theoretical statisticians [76], who claimed that a simple method being a special case of e.g. ARIMA models, could not be more accurate than the ARIMA one, refusing to accept the empirical evidence proving the opposite.

A co-author of the paper is Spyros Makridakis, currently with nearly 17K Google Scholar citations.