Multi-horizon time-series forecasting involves simultaneously making predictions for a consecutive sequence of subsequent time steps. This task arises in many application domains, such as healthcare and finance, where mispredictions can have a high cost and reduce trust. The learning with abstention framework tackles these problems by allowing a model to abstain from offering a prediction when it is at an elevated risk of making a misprediction. Unfortunately, existing abstention strategies are ill-suited for the multi-horizon setting: they target problems where a model offers a single prediction for each instance. Hence, they ignore the structured and correlated nature of the predictions offered by a multi-horizon forecaster. We formalize the problem of learning with abstention for multi-horizon forecasting setting and show that its structured nature admits a richer set of abstention problems. Concretely, we propose three natural notions of how a model could abstain for multi-horizon forecasting. We theoretically analyze each problem to derive the optimal abstention strategy and propose an algorithm that implements it. Extensive evaluation on 24 datasets shows that our proposed algorithms significantly outperforms existing baselines.

翻译：多时间范围时序预测涉及对连续后续时间步序列同时进行预测。该任务出现在医疗健康与金融等诸多应用领域，其中预测错误可能带来高昂代价并降低信任度。带弃权的学习框架通过允许模型在误预测风险较高时放弃提供预测来解决此类问题。然而，现有弃权策略并不适用于多时间范围场景：它们针对的是模型为每个实例提供单一预测的问题，因而忽略了多范围预测器所提供预测的结构化与相关性特征。我们形式化了多范围预测场景下的带弃权学习问题，并证明其结构化特性可衍生出更丰富的弃权问题类型。具体而言，我们提出了三种适用于多范围预测的模型弃权机制。我们通过理论分析为每个问题推导出最优弃权策略，并提出实现该策略的算法。在24个数据集上的广泛评估表明，我们提出的算法显著优于现有基线方法。