In many organisations, accurate forecasts are essential for making informed decisions for a variety of applications from inventory management to staffing optimization. Whatever forecasting model is used, changes in the underlying process can lead to inaccurate forecasts, which will be damaging to decision-making. At the same time, models are becoming increasingly complex and identifying change through direct modelling is problematic. We present a novel framework for online monitoring of forecasts to ensure they remain accurate. By utilizing sequential changepoint techniques on the forecast errors, our framework allows for the real-time identification of potential changes in the process caused by various external factors. We show theoretically that some common changes in the underlying process will manifest in the forecast errors and can be identified faster by identifying shifts in the forecast errors than within the original modelling framework. Moreover, we demonstrate the effectiveness of this framework on numerous forecasting approaches through simulations and show its effectiveness over alternative approaches. Finally, we present two concrete examples, one from Royal Mail parcel delivery volumes and one from NHS A\&E admissions relating to gallstones.

翻译：在许多组织中，准确的预测对于从库存管理到人员配置优化等各种应用的明智决策至关重要。无论采用何种预测模型，底层过程的变化都可能导致预测不准确，从而损害决策质量。与此同时，模型正变得越来越复杂，通过直接建模识别变化存在困难。本文提出了一种用于在线监测预测准确性的新颖框架，以确保预测持续可靠。通过对预测误差应用序贯变点检测技术，该框架能够实时识别由各种外部因素引起的潜在过程变化。我们从理论上证明，底层过程中的某些常见变化会体现在预测误差中，并且通过检测预测误差的偏移，能够比在原始建模框架内更快地识别这些变化。此外，我们通过模拟在多种预测方法上验证了该框架的有效性，并展示了其相对于替代方法的优势。最后，我们提供了两个具体案例：一个是皇家邮政包裹投递量的预测，另一个是英国国家医疗服务体系关于胆结石相关急诊入院人数的预测。