DGCformer：面向多变量时间序列预测的深度图聚类Transformer
DGCformer: Deep Graph Clustering Transformer for Multivariate Time Series Forecasting
AI总结：
针对多变量时间序列预测中通道依赖策略易引入无关变量致性能下降的问题，提出结合通道依赖与独立策略优势的DGCformer，经图卷积自编码器分组变量并搭配前后掩码自注意力，在8个数据集上表现优于现有最优模型。
摘要：
多变量时间序列预测任务通常采用通道依赖（CD）方式开展，因为该方式能纳入更多变量相关信息。但它也可能引入大量无关变量，甚至导致性能比通道独立（CI）策略更差。本文结合两种策略的优势，提出用于多变量时间序列预测的深度图聚类Transformer（DGCformer）。具体而言，该方法首先通过集成自编码器的图卷积网络对相关变量进行分组，随后采用前后掩码自注意力机制，对每组变量应用CD策略，对不同组则应用CI策略。在8个数据集上的大量实验结果表明，该方法优于现有最优模型，代码将在论文录用后公开。
英文摘要：
Multivariate time series forecasting tasks are usually conducted in a channel-dependent (CD) way since it can incorporate more variable-relevant information. However, it may also involve a lot of irrelevant variables, and this even leads to worse performance than the channel-independent (CI) strategy. This paper combines the strengths of both strategies and proposes the Deep Graph Clustering Transformer (DGCformer) for multivariate time series forecasting. Specifically, it first groups these relevant variables by a graph convolutional network integrated with an autoencoder, and a former-latter masked self-attention mechanism is then considered with the CD strategy being applied to each group of variables while the CI one for different groups. Extensive experimental results on eight datasets demonstrate the superiority of our method against state-of-the-art models, and our code will be publicly available upon acceptance.
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。
