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

• URL: http://arxiv.org/abs/2312.06220v1
• Date: Mon, 11 Dec 2023 09:10:38 GMT
• Title: Dance of Channel and Sequence: An Efficient Attention-Based Approach for Multivariate Time Series Forecasting
• Authors: Haoxin Wang, Yipeng Mo, Nan Yin, Honghe Dai, Bixiong Li, Songhai Fan, Site Mo
• Abstract summary: CSformer is an innovative framework characterized by a meticulously engineered two-stage self-attention mechanism. We introduce sequence adapters and channel adapters, ensuring the model's ability to discern salient features across various dimensions.
• Score: 3.372816393214188
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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