In recent developments, predictive models for multivariate time series analysis have exhibited commendable performance through the adoption of the prevalent principle of channel independence. Nevertheless, it is imperative to acknowledge the intricate interplay among channels, which fundamentally influences the outcomes of multivariate predictions. Consequently, the notion of channel independence, while offering utility to a certain extent, becomes increasingly impractical, leading to information degradation. In response to this pressing concern, we present CSformer, an innovative framework characterized by a meticulously engineered two-stage self-attention mechanism. This mechanism is purposefully designed to enable the segregated extraction of sequence-specific and channel-specific information, while sharing parameters to promote synergy and mutual reinforcement between sequences and channels. Simultaneously, we introduce sequence adapters and channel adapters, ensuring the model's ability to discern salient features across various dimensions. Rigorous experimentation, spanning multiple real-world datasets, underscores the robustness of our approach, consistently establishing its position at the forefront of predictive performance across all datasets. This augmentation substantially enhances the capacity for feature extraction inherent to multivariate time series data, facilitating a more comprehensive exploitation of the available information.

翻译：近年来，多变量时间序列分析的预测模型通过采用广泛流行的通道独立原则展现出令人瞩目的性能。然而，必须认识到通道之间错综复杂的相互作用从根本上影响着多变量预测的结果。因此，通道独立的概念虽然在一定程度上具有价值，但变得越来越不切实际，导致信息退化。针对这一紧迫问题，我们提出了CSformer，这是一个创新框架，其核心是精心设计的两阶段自注意力机制。该机制旨在实现序列特定信息和通道特定信息的分离提取，同时通过共享参数促进序列与通道之间的协同与相互增强。与此同时，我们引入了序列适配器和通道适配器，确保模型能够识别不同维度上的显著特征。跨越多个真实世界数据集的严格实验证明了我们方法的稳健性，使其在所有数据集上均稳居预测性能的前沿。这种增强显著提升了多变量时间序列数据固有的特征提取能力，从而更全面地利用了可用信息。