Evolved differential model for sporadic graph time-series prediction  

作　　者：Yucheng Xing Jacqueline Wu Yingru Liu Xuewen Yang Xin Wang 

机构地区：[1]Department of Electrical and Computer Engineering,Stony Brook University,Stony Brook,NY 11794,USA [2]New York University,New York,NY 10012,USA

出　　处：《Intelligent and Converged Networks》2024年第3期237-247,共11页智能与融合网络（英文）

摘　　要：Sensing signals of many real-world network systems,such as traffic network or microgrid,could be sparse and irregular in both spatial and temporal domains due to reasons such as cost reduction,noise corruption,or device malfunction.It is a fundamental but challenging problem to model the continuous dynamics of a system from the sporadic observations on the network of nodes,which is generally represented as a graph.In this paper,we propose a deep learning model called Evolved Differential Model(EDM)to model the continuous-time stochastic process from partial observations on graph.Our model incorporates diffusion convolutional network to parameterize continuous-time system dynamics by graph Ordinary Differential Equation(ODE)and graph Stochastic Differential Equation(SDE).The graph ODE is applied to accurately capture the spatial-temporal relation and extract hidden features from the data.The graph SDE can efficiently capture the underlying uncertainty of the network systems.With the recurrent ODE-SDE scheme,EDM can serve as an accurate online predictive model that is effective for either monitoring or analyzing the real-world networked objects.Through extensive experiments on several datasets,we demonstrate that EDM outperforms existing methods in online prediction tasks.

关 键 词：graph sequence prediction sporadic time series continuous model stochastic model differential equation 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论]