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

Abstract

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. Our aim is to develop a unified end-to-end model that takes a time series as input, produces a forecast, accounts for its predictive uncertainty, and makes a downstream classification decision, enabling models to support or automate such temporal decision-making tasks. Related problems arise across a range of applications, yet the literature lacks both a unified methodology and a formal problem statement. By formalizing the task, we aim to stimulate research on such models and encourage cross-domain collaboration. To solve the Foreclassing problem, we propose a deep Bayesian neural network, ForeClassNet. As part of this framework, we introduce a new type of neural network layer, Boltzmann convolutions, which enable probabilistic learning of kernel sizes in convolutional layers. We evaluate the Foreclassing framework against standard time series classification methods and demonstrate the efficacy of ForeClassNet on real-world Foreclassing datasets from the weather, energy, and finance domains, achieving superior performance relative to state-of-the-art time series classifiers.