SCFormer：面向多元时间序列预测的带累积历史状态的结构化通道级Transformer
SCFormer: Structured Channel-wise Transformer with Cumulative Historical State for Multivariate Time Series Forecasting
AI总结：
SCFormer通过在Transformer的线性变换中引入时间约束并利用HiPPO处理累积历史状态，解决了通道级自注意力缺乏时间约束和未充分利用历史序列的问题，在多元时间序列预测上显著优于主流基线。
摘要：
Transformer模型通过利用通道级自注意力在多元时间序列预测中表现出强劲性能。然而，该方法在计算时间特征时缺乏时间约束，并且未能有效利用累积历史序列。为解决这些局限性，我们提出了带累积历史状态的结构化通道级Transformer（SCFormer）。SCFormer对所有线性变换引入时间约束，包括查询、键和值矩阵，以及Transformer内部的全连接层。此外，SCFormer采用高阶多项式投影算子（HiPPO）处理累积历史时间序列，使模型能够在预测时纳入超出回看窗口的信息。在多个真实世界数据集上的大量实验表明，SCFormer显著优于主流基线方法，凸显了其在增强时间序列预测方面的有效性。代码已在 https://github.com/ShiweiGuo1995/SCFormer 公开。
英文摘要：
The Transformer model has shown strong performance in multivariate time series forecasting by leveraging channel-wise self-attention. However, this approach lacks temporal constraints when computing temporal features and does not utilize cumulative historical series effectively.To address these limitations, we propose the Structured Channel-wise Transformer with Cumulative Historical state (SCFormer). SCFormer introduces temporal constraints to all linear transformations, including the query, key, and value matrices, as well as the fully connected layers within the Transformer. Additionally, SCFormer employs High-order Polynomial Projection Operators (HiPPO) to deal with cumulative historical time series, allowing the model to incorporate information beyond the look-back window during prediction. Extensive experiments on multiple real-world datasets demonstrate that SCFormer significantly outperforms mainstream baselines, highlighting its effectiveness in enhancing time series forecasting. The code is publicly available at https://github.com/ShiweiGuo1995/SCFormer
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。
