Deep Coupling Network For Multivariate Time Series Forecasting

IF 5.4 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Kun Yi, Qi Zhang, Hui He, Kaize Shi, Liang Hu, Ning An, Zhendong Niu
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引用次数: 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.

用于多变量时间序列预测的深度耦合网络
多变量时间序列(MTS)预测在现实世界的许多应用中都至关重要。要实现准确的 MTS 预测,必须同时考虑时间序列数据之间的序列内和序列间关系。然而,以往的研究通常将序列内和序列间关系分开建模,忽略了时间序列数据内部和之间存在的多阶交互作用,这会严重降低预测精度。在本文中,我们从互信息的角度重新审视了序列内和序列间的关系,并据此构建了一个全面的关系学习机制,以同时捕捉错综复杂的多阶序列内和序列间耦合。基于该机制,我们提出了一种用于 MTS 预测的新型深度耦合网络,并将其命名为 DeepCN。DeepCN 由一个耦合机制、一个耦合变量表示模块和一个推理模块组成,耦合机制致力于同时探索时间序列数据之间的多阶序列内和序列间关系,耦合变量表示模块旨在编码多样化的变量模式,而推理模块则通过一个前向步骤实现预测。在七个真实世界数据集上进行的广泛实验表明,与最先进的基线相比,我们提出的 DeepCN 实现了更优越的性能。
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来源期刊
ACM Transactions on Information Systems
ACM Transactions on Information Systems 工程技术-计算机:信息系统
CiteScore
9.40
自引率
14.30%
发文量
165
审稿时长
>12 weeks
期刊介绍: The ACM Transactions on Information Systems (TOIS) publishes papers on information retrieval (such as search engines, recommender systems) that contain: new principled information retrieval models or algorithms with sound empirical validation; observational, experimental and/or theoretical studies yielding new insights into information retrieval or information seeking; accounts of applications of existing information retrieval techniques that shed light on the strengths and weaknesses of the techniques; formalization of new information retrieval or information seeking tasks and of methods for evaluating the performance on those tasks; development of content (text, image, speech, video, etc) analysis methods to support information retrieval and information seeking; development of computational models of user information preferences and interaction behaviors; creation and analysis of evaluation methodologies for information retrieval and information seeking; or surveys of existing work that propose a significant synthesis. The information retrieval scope of ACM Transactions on Information Systems (TOIS) appeals to industry practitioners for its wealth of creative ideas, and to academic researchers for its descriptions of their colleagues'' work.
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