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Bonferroni's correction on hold-out data is an excellent suggestion. To adapt it into time series forecasting, one could perform temporal cross-validation with rolling windows and follow the performance's variance through time.

Unfortunately, the computational time would explode if the ML method's optimization is performed naively. Precise measurements of the statistical significance would crowd out researchers except for Big Tech.



Bonferroni is probably not the right choice because it can be overly conservative, especially if the tests are positively-correlated.

Holm-Šidák would be better--but something like false discovery rate might be easier to interpret.




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