基于伪标签触发的预测误差重训练用于在线时间序列预测

中文摘要 AI 辅助

真实世界的时间序列预测系统在非平稳数据流下运行，其预测性能可能随时间推移而下降。尽管重训练可以恢复性能，但会带来不可忽视的计算和操作成本。在部署资源有限的情况下，关键挑战不仅在于如何重训练，还在于何时重训练。现有的重训练策略往往依赖漂移警报或模型陈旧度等间接指标，而我们则利用已实现的预测误差作为直接的部署反馈。本文提出PILOT（伪标签信息学习的在线触发机制），这是一个在线重训练框架，它从预测误差动态中学习何时进行重训练。由于缺乏真实的重训练标签，PILOT从未来预测误差的增加中构建伪标签，并训练一个轻量级评分器，根据观测到的误差状态来预测该伪标签。在部署时，PILOT仅使用已完成的预测误差，并作为任意预测主干的即插即用模块，无需修改架构。我们在八个基准数据集上，使用三种代表性主干模型——DLinear、iTransformer和TimesNet，在标准多变量预测设置下评估PILOT。在全部三种主干模型上，PILOT在重训练策略中实现了最先进的平均排名性能，同时保持了良好的性能-效率权衡。

英文摘要

Real-world time series forecasting systems operate under non-stationary data streams, where forecasting performance may degrade over time. Although retraining can recover the performance, it incurs non-trivial computational and operational costs. Under limited deployment resources, the key challenge is therefore not only how to retrain but also when to retrain. While existing retraining policies often rely on indirect indicators such as drift alarms or model staleness, we instead use realized forecast errors as direct deployment feedback. In this paper, we propose PILOT (Pseudo-label-Informed Learned Online Trigger), an online retraining framework that learns when to retrain from forecast-error dynamics. Since ground-truth retraining labels are unavailable, PILOT constructs a pseudo-label from future increases in forecast error and trains a lightweight scorer to predict it from observed error states. At deployment, PILOT uses only completed forecast errors and serves as a plug-in module for arbitrary forecasting backbones without architectural modification. We evaluate PILOT under standard multivariate forecasting settings across eight benchmarks with three representative backbones---DLinear, iTransformer, and TimesNet. Across all three backbones, PILOT achieves state-of-the-art average-rank performance among retraining policies while maintaining a favorable performance--efficiency trade-off.

发表机构

• Seoul National University（首尔大学）

机构由 AI 辅助整理，请以论文原文为准。