Deep Coupling Network For Multivariate Time Series Forecasting

• URL: http://arxiv.org/abs/2402.15134v1
• Date: Fri, 23 Feb 2024 06:38:08 GMT
• Title: Deep Coupling Network For Multivariate Time Series Forecasting
• Authors: Kun Yi, Qi Zhang, Hui He, Kaize Shi, Liang Hu, Ning An, Zhendong Niu
• Abstract summary: We propose a novel deep coupling network for MTS forecasting, named DeepCN. Our proposed DeepCN achieves superior performance compared with the state-of-the-art baselines.
• Score: 24.01637416183444
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multivariate time series (MTS) forecasting is crucial in many real-world applications. To achieve accurate MTS forecasting, it is essential to simultaneously consider both intra- and inter-series relationships among time series data. However, previous work has typically modeled intra- and inter-series relationships separately and has disregarded multi-order interactions present within and between time series data, which can seriously degrade forecasting accuracy. In this paper, we reexamine intra- and inter-series relationships from the perspective of mutual information and accordingly construct a comprehensive relationship learning mechanism tailored to simultaneously capture the intricate multi-order intra- and inter-series couplings. Based on the mechanism, we propose a novel deep coupling network for MTS forecasting, named DeepCN, which consists of a coupling mechanism dedicated to explicitly exploring the multi-order intra- and inter-series relationships among time series data concurrently, a coupled variable representation module aimed at encoding diverse variable patterns, and an inference module facilitating predictions through one forward step. Extensive experiments conducted on seven real-world datasets demonstrate that our proposed DeepCN achieves superior performance compared with the state-of-the-art baselines.

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