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.

翻译：时间序列预测被广泛用于辅助决策。人类决策者解读这些预测，结合先验经验和对未来结果的不确定性，而后做出决策。本文提出一个名为“前分类”的新机器学习问题，该问题针对旨在自动化此类决策过程中人类参与的场景。我们的目标是开发一个统一的端到端模型，该模型以时间序列为输入，生成预测，考虑其预测不确定性，并做出下游分类决策，使模型能够支持或自动化此类时序决策任务。相关问题在众多应用中均有出现，然而文献中既缺乏统一的方法论，也缺少正式的问题定义。通过形式化该任务，我们旨在激发对此类模型的研究，并鼓励跨领域合作。为解决前分类问题，我们提出深度贝叶斯神经网络ForeClassNet。作为该框架的一部分，我们引入一种新型神经网络层——玻尔兹曼卷积，该层能够实现卷积层中核大小的概率学习。我们将前分类框架与标准时间序列分类方法进行评估，并在来自天气、能源和金融领域的真实前分类数据集上验证了ForeClassNet的有效性，相较于最先进的时间序列分类器取得了更优性能。