{"title":"Adaptive Graph Convolution Neural Differential Equation for Spatio-Temporal Time Series Prediction","authors":"Min Han;Qipeng Wang","doi":"10.1109/TKDE.2024.3383895","DOIUrl":null,"url":null,"abstract":"Multivariate time series prediction has aroused widely research interests during decades. However, the spatial heterogeneity and temporal evolution characteristics bring much challenges for high-dimensional time series prediction. In this paper, a novel adaptive graph convolution module is introduced to automatically learn the spatial correlation of multivariate time series and a Koopman-based neural differential equation is proposed to simulate the nonlinear system state evolution. In detail, the correlation between multivariate time series is revealed by the consine similarity of node embedding to infer the potential relationship between nodes and the spatio-temporal feature fusion module is utilized. The LSTM-based network is adopted as Koopman operator to reveal the latent states of spatio-temporal time series and the reversible assumption is imposed on the Koopman operator. Furthermore, the Euler-trapezoidal integration are utilized to simulate the temporal dynamics and multiple-step prediction is carried out in the latent space from the perspective of dynamical differential equation. The proposed model could explicitly discover the spatial correlation by adaptive graph convolution and reveal the temporal dynamics by neural differential equation, which make the modeling more interpretable. Simulation results show the effectiveness on spatio-temporal dynamic discovery and prediction performance.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 6","pages":"3193-3204"},"PeriodicalIF":8.9000,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Knowledge and Data Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10487888/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Multivariate time series prediction has aroused widely research interests during decades. However, the spatial heterogeneity and temporal evolution characteristics bring much challenges for high-dimensional time series prediction. In this paper, a novel adaptive graph convolution module is introduced to automatically learn the spatial correlation of multivariate time series and a Koopman-based neural differential equation is proposed to simulate the nonlinear system state evolution. In detail, the correlation between multivariate time series is revealed by the consine similarity of node embedding to infer the potential relationship between nodes and the spatio-temporal feature fusion module is utilized. The LSTM-based network is adopted as Koopman operator to reveal the latent states of spatio-temporal time series and the reversible assumption is imposed on the Koopman operator. Furthermore, the Euler-trapezoidal integration are utilized to simulate the temporal dynamics and multiple-step prediction is carried out in the latent space from the perspective of dynamical differential equation. The proposed model could explicitly discover the spatial correlation by adaptive graph convolution and reveal the temporal dynamics by neural differential equation, which make the modeling more interpretable. Simulation results show the effectiveness on spatio-temporal dynamic discovery and prediction performance.
期刊介绍:
The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.