面向可解释Transformer时间序列预测的时序显著性检测
Temporal Saliency Detection Towards Explainable Transformer-based Timeseries Forecasting
AI总结：
本文提出时序显著性检测（TSD）方法，通过在Transformer编码器中引入受U-Net启发的信息收缩与扩展模块，结合注意力机制实现显著性模式的多分辨率分析，有效提升了多视界时间序列预测的可解释性与准确性。
摘要：
尽管众多基于Transformer的模型取得了显著进展，但长期多视界时间序列预测任务仍是一项持续的挑战，尤其是在可解释性方面。聚焦于解释DNN时常用的显著性图，我们的目标是构建基于注意力的架构，通过与合适的注意力头建立联系，自动编码与显著性相关的时序模式。因此，本文引入了时序显著性检测（TSD），这是一种基于注意力机制并将其应用于多视界时间序列预测的有效方法。我们提出的架构虽遵循通用的编码器-解码器结构，但在编码器组件上进行了重大改造，其中融入了一系列受U-Net风格架构启发的信息收缩与扩展模块。TSD方法通过压缩多头促进显著性模式的多分辨率分析，从而逐步增强对复杂时间序列数据的预测能力。实证评估表明，在多种远视界预测设置下，与其他模型相比，我们提出的方法在多个标准基准数据集上展现出优越性。在多变量和单变量预测背景下，初始TSD相较于多个模型分别实现了31%和46%的显著相对提升。我们相信本研究提出的综合调查将为未来的研究工作提供有价值的见解与益处。
英文摘要：
Despite the notable advancements in numerous Transformer-based models, the task of long multi-horizon time series forecasting remains a persistent challenge, especially towards explainability. Focusing on commonly used saliency maps in explaining DNN in general, our quest is to build attention-based architecture that can automatically encode saliency-related temporal patterns by establishing connections with appropriate attention heads. Hence, this paper introduces Temporal Saliency Detection (TSD), an effective approach that builds upon the attention mechanism and applies it to multi-horizon time series prediction. While our proposed architecture adheres to the general encoder-decoder structure, it undergoes a significant renovation in the encoder component, wherein we incorporate a series of information contracting and expanding blocks inspired by the U-Net style architecture. The TSD approach facilitates the multiresolution analysis of saliency patterns by condensing multi-heads, thereby progressively enhancing the forecasting of complex time series data. Empirical evaluations illustrate the superiority of our proposed approach compared to other models across multiple standard benchmark datasets in diverse far-horizon forecasting settings. The initial TSD achieves substantial relative improvements of 31% and 46% over several models in the context of multivariate and univariate prediction. We believe the comprehensive investigations presented in this study will offer valuable insights and benefits to future research endeavors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
