Adaptive Graph Convolution Neural Differential Equation for Spatio-Temporal Time Series Prediction

IF 8.9 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Min Han;Qipeng Wang
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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.
用于时空时间序列预测的自适应图卷积神经微分方程
近几十年来,多元时间序列预测引起了广泛的研究兴趣。然而,其空间异质性和时间演化特征给高维时间序列预测带来了诸多挑战。本文引入了一种新的自适应图卷积模块来自动学习多变量时间序列的空间相关性,并提出了一种基于koopman的神经微分方程来模拟非线性系统状态演化。通过节点嵌入的连续相似度来揭示多变量时间序列之间的相关性,推断节点之间的潜在关系,并利用时空特征融合模块。采用基于lstm的网络作为库普曼算子来揭示时空时间序列的潜在状态,并对库普曼算子施加可逆假设。利用欧拉-梯形积分法模拟时间动力学,从动力学微分方程的角度对潜在空间进行多步预测。该模型可以通过自适应图卷积清晰地发现空间相关性,并通过神经微分方程揭示时间动态,使模型更具可解释性。仿真结果表明了该方法在时空动态发现和预测方面的有效性。
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来源期刊
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering 工程技术-工程:电子与电气
CiteScore
11.70
自引率
3.40%
发文量
515
审稿时长
6 months
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.
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