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Maximizing Forecast Value Added through Machine Learning and “Nudges” by Jeff Baker
Is it possible to determine in advance whether a certain action will add enough value to justify its cost? This is the question addressed in our feature article. The author applies machine-learning methods to predict the FVA of a judgmental override from the characteristics of the override—its direction and size—along with the forecastability of the series. The article introduces several new constructs: the threshold FVA, the dispersion-scaled override, and the use of nudges rather than rigid rules to prompt forecasters to consider the desirability of overriding a statistical forecast. Robert Fildes and Michael Gilliland provide Commentaries.