  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.
