Rolling origin forecast instability refers to variability in forecasts for a specific period induced by updating the forecast when new data points become available. Recently, an extension to the N-BEATS model for univariate time series point forecasting was proposed to include forecast stability as an additional optimization objective, next to accuracy. It was shown that more stable forecasts can be obtained without harming accuracy by minimizing a composite loss function that contains both a forecast error and a forecast instability component, with a static hyperparameter to control the impact of stability. In this paper, we empirically investigate whether further improvements in stability can be obtained without compromising accuracy by applying dynamic loss weighting algorithms, which change the loss weights during training. We show that some existing dynamic loss weighting methods achieve this objective. However, our proposed extension to the Random Weighting approach -- Task-Aware Random Weighting -- shows the best performance.

翻译：滚动原点预测不稳定性指的是在获得新数据点时更新预测所引发的针对特定时期预测结果的变异性。近期，针对单变量时间序列点预测的N-BEATS模型提出了扩展方案，将预测稳定性作为除准确性之外的额外优化目标。研究表明，通过最小化同时包含预测误差和预测不稳定分量的复合损失函数（采用静态超参数控制稳定性影响），可以在不损害准确性的前提下获得更稳定的预测结果。本文通过实证研究探讨了在训练过程中动态调整损失权重的算法是否能在保持准确性的同时进一步提升预测稳定性。实验表明，部分现有动态损失加权方法能够实现该目标。然而，我们提出的随机加权方法扩展方案——任务感知随机加权——展现出最佳性能。