多元统计
计算机科学
系列(地层学)
时间序列
联轴节(管道)
人工智能
数据挖掘
计量经济学
算法
机器学习
数学
地质学
机械工程
古生物学
工程类
作者
Kun Yi,Qi Zhang,Hui He,Kaize Shi,Liang Hu,Ning An,Zhendong Niu
出处
期刊:ACM Transactions on Information Systems
日期:2024-04-27
卷期号:42 (5): 1-28
摘要
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 article, 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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