CSformer: Combining Channel Independence and Mixing for Robust Multivariate Time Series Forecasting
This work addresses a specific challenge in multivariate time series analysis for applications like forecasting, but it is incremental as it builds on existing channel independence methods.
The paper tackles the problem of information loss in multivariate time series forecasting due to channel independence by proposing CSformer, a framework that combines channel independence with mixing, achieving state-of-the-art results on real-world datasets.
In the domain of multivariate time series analysis, the concept of channel independence has been increasingly adopted, demonstrating excellent performance due to its ability to eliminate noise and the influence of irrelevant variables. However, such a concept often simplifies the complex interactions among channels, potentially leading to information loss. To address this challenge, we propose a strategy of channel independence followed by mixing. Based on this strategy, we introduce CSformer, a novel framework featuring a two-stage multiheaded self-attention mechanism. This mechanism is designed to extract and integrate both channel-specific and sequence-specific information. Distinctively, CSformer employs parameter sharing to enhance the cooperative effects between these two types of information. Moreover, our framework effectively incorporates sequence and channel adapters, significantly improving the model's ability to identify important information across various dimensions. Extensive experiments on several real-world datasets demonstrate that CSformer achieves state-of-the-art results in terms of overall performance.