动态调整损失权重，让时间序列预测更稳定且不牺牲准确率。
Using dynamic loss weighting to boost improvements in forecast stability
- 用动态损失权重替代固定权重，优化预测稳定性。
- 实验表明新方法在不降低准确率前提下显著提升预测稳定性。
- 适合关注模型鲁棒性与长期预测可靠性的研究者。
滚动原点预测不稳定是指当新数据出现时，对特定时期预测结果的波动。近期有研究在N-BEATS模型基础上引入预测稳定性作为额外优化目标，通过最小化包含预测误差与不稳定性的复合损失函数，在保持准确率的同时获得更稳定的预测。本文实证研究发现，采用动态损失权重算法（训练中调整损失权重）可进一步提升稳定性而不损害准确率。现有动态权重方法已能实现该目标，并揭示其有效原因。此外，本文提出一种改进的随机权重方法——任务感知随机权重（Task-Aware Random Weighting），同样实现了稳定性提升。
原文摘要 · Abstract (English)
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 existing dynamic loss weighting methods can achieve this objective and provide insights into why this might be the case. Additionally, we propose an extension to the Random Weighting approach -- Task-Aware Random Weighting -- which also achieves this objective.
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