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In recent developments, predictive models for multivariate time seriesanalysis have exhibited commendable performance through the adoption of theprevalent principle of channel independence. Nevertheless, it is imperative toacknowledge the intricate interplay among channels, which fundamentallyinfluences the outcomes of multivariate predictions. Consequently, the notionof channel independence, while offering utility to a certain extent, becomesincreasingly impractical, leading to information degradation. In response tothis pressing concern, we present CSformer, an innovative frameworkcharacterized by a meticulously engineered two-stage self-attention mechanism.This mechanism is purposefully designed to enable the segregated extraction ofsequence-specific and channel-specific information, while sharing parameters topromote synergy and mutual reinforcement between sequences and channels.Simultaneously, we introduce sequence adapters and channel adapters, ensuringthe model's ability to discern salient features across various dimensions.Rigorous experimentation, spanning multiple real-world datasets, underscoresthe robustness of our approach, consistently establishing its position at theforefront of predictive performance across all datasets. This augmentationsubstantially enhances the capacity for feature extraction inherent tomultivariate time series data, facilitating a more comprehensive exploitationof the available information.

Dance of Channel and Sequence: An Efficient Attention-Based Approach for Multivariate Time Series Forecasting