English(EN) Pseudo-Label-Triggered Retraining from Forecast Errors for Online Time Series Forecasting

新的PILOT框架优化在线时间序列预测重训

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-01 04:00

研究人员开发了PILOT（Pseudo-label-Informed Learned Online Trigger，伪标签信息学习在线触发器），一个旨在优化在线时间序列预测系统重训的新型框架。该系统根据观察到的预测误差学习何时进行重训，而不是依赖漂移警报等间接指标。PILOT从未来的预测误差增加中构建伪标签，并训练一个轻量级评分器来预测该标签，使其可以作为即插即用模块用于各种预测骨干网络。在DLinear、iTransformer和TimesNet的八个基准测试中的评估表明，PILOT在平衡效率和有效性的同时，实现了最先进的平均排名性能。 AI

影响该框架通过优化重训过程，有望提高依赖时间序列预测的现实世界AI系统的效率和准确性。

排序理由这是一篇详细介绍时间序列预测新框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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本文如何被排名

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Yeryeong Kwak, Yoo-Min Jung, Jonghun Park · 2026-10-01 04:00

伪标签触发的基于预测误差的在线时间序列预测再训练

arXiv:2609.39789v1 Announce Type: cross Abstract: 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 op…

本文如何被排名

报道来源 [1]

伪标签触发的基于预测误差的在线时间序列预测再训练

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