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

TL;DR

CSformer is a novel multivariate time series forecasting framework that combines channel independence with mixing, using a two-stage multiheaded self-attention mechanism to effectively capture and integrate channel-specific and sequence-specific information, achieving state-of-the-art results.

Contribution

The paper introduces CSformer, a new model that enhances channel independence strategies with a mixing approach and a two-stage attention mechanism for improved forecasting accuracy.

Findings

Achieves state-of-the-art performance on real-world datasets.

Effectively captures both channel-specific and sequence-specific information.

Improves robustness and accuracy in multivariate time series forecasting.

Abstract

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.