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In the domain of multivariate time series analysis, the concept of channelindependence has been increasingly adopted, demonstrating excellent performancedue to its ability to eliminate noise and the influence of irrelevantvariables. However, such a concept often simplifies the complex interactionsamong channels, potentially leading to information loss. To address thischallenge, we propose a strategy of channel independence followed by mixing.Based on this strategy, we introduce CSformer, a novel framework featuring atwo-stage multiheaded self-attention mechanism. This mechanism is designed toextract and integrate both channel-specific and sequence-specific information.Distinctively, CSformer employs parameter sharing to enhance the cooperativeeffects between these two types of information. Moreover, our frameworkeffectively incorporates sequence and channel adapters, significantly improvingthe model's ability to identify important information across variousdimensions. Extensive experiments on several real-world datasets demonstratethat CSformer achieves state-of-the-art results in terms of overallperformance.

CSformer: Combining Channel Independence and Mixing for Robust Multivariate Time Series Forecasting