Forecast stability, that is, the consistency of predictions over time, is essential in business settings where sudden shifts in forecasts can disrupt planning and erode trust in predictive systems. Despite its importance, stability is often overlooked in favor of accuracy. In this study, we evaluate the stability of point and probabilistic forecasts across several retraining scenarios using three large forecastingdatasets and ten different global forecasting models. To analyze stability in the probabilistic setting, we propose a new model-agnostic, distribution-free, and scale-free metric that measuresprobabilistic stability: the Scaled Multi-Quantile Change (SMQC). The results show that less frequent retraining not only preserves but often improves forecast stability, challenging the need for frequent retraining. Moreover, the study shows that accuracy and stability are not necessarily conflicting objectives when adopting a global modeling approach. The study promotes a shift toward stability-aware forecasting practices, proposing a new metric to evaluate forecast stability effectively in probabilistic settings, and offering practical guidelines for building more stable and sustainable forecasting systems.

翻译：预测稳定性，即预测结果随时间的一致性，在商业环境中至关重要，因为预测结果的突然变化会扰乱规划并削弱对预测系统的信任。尽管稳定性非常重要，但在实际应用中往往因追求准确性而被忽视。本研究使用三个大型预测数据集和十种不同的全局预测模型，评估了多种重训练场景下点预测和概率预测的稳定性。为分析概率预测环境中的稳定性，我们提出了一种新的、与模型无关、无需分布假设且无量纲依赖的概率稳定性度量指标：尺度化多分位数变化（SMQC）。结果表明，较低频率的重训练不仅能保持预测稳定性，甚至常常能提升稳定性，这对频繁重训练的必要性提出了挑战。此外，研究还表明，在采用全局建模方法时，准确性与稳定性并非必然冲突的目标。本研究推动了向稳定性感知预测实践的转变，提出了一种在概率环境中有效评估预测稳定性的新指标，并为构建更稳定、可持续的预测系统提供了实用指导。