Foreclassing: From Forecasts to Decisions
Read the original on arXiv Statistics ML →The paper introduces Foreclassing, a new machine learning problem where the class label of a time series is defined by its future observations. It provides a mathematical formulation, theoretical results on how future information influences Bayes risk and classification error, and proposes ForeClassNet, a neural network that jointly forecasts and classifies while accounting for uncertainty. Experiments across weather, energy, and finance datasets show that Foreclassing can improve classification accuracy, probabilistic predictions, and downstream decision-making compared to conventional methods.
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