English(EN) When Attention Does Not Explain the Peak: Temporal Reference vs. Forecast Output in Attention-Based Time-Series Forecasting

时间序列预测中的注意力图可能无法解释峰值预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-07 04:00

一项新的研究论文挑战了对时间序列预测模型中注意力图的普遍解释。研究发现，尽管在负荷预测模型中，注意力图结构良好且对未来天气数据敏感，但它们并不能准确解释预测峰值的时间。该模型的预测比注意力图所显示的更准确，这表明注意力可能在整合未来信息方面起着内部参考作用，而不是直接解释峰值预测。 AI

影响挑战了对注意力机制的普遍解释，可能影响研究人员分析和开发时间序列预测模型的方式。

排序理由在arXiv上发表的研究论文，详细介绍了关于时间序列预测中注意力机制的发现。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Yuji Akamatsu, Takao Yamanaka · 2026-10-07 04:00

当注意力无法解释峰值时：基于注意力的时间序列预测中的时间参考与预测输出

arXiv:2610.07080v1 Announce Type: new Abstract: Attention maps are often interpreted as evidence of what a forecasting model uses when making predictions. In our load-forecasting model, a CLS representation of historical demand queries 24 future exogenous horizon tokens through c…