Foreclassing: A new machine learning perspective on human decision making with temporal data

ArXi:2503.04956v2 Announce Type: replace-cross Time series forecasts are widely used to inform decisions. Human decision-makers interpret these forecasts, incorporate prior experience and uncertainty about future outcomes, and then make a decision. In this paper, we propose a new machine learning problem, which we call Foreclassing, which addresses settings in which the aim is to automate human involvement in such decision-making processes.