Multivariate Time Series Forecasting With Dynamic Graph Neural ODEs
Ming Jin, Yu Zheng, Yuan-Fang Li, Siheng Chen, Bin Yang, Shirui Pan · IEEE Transactions on Knowledge and Data Engineering · 2022
Multivariate time series forecasting has long received significant attention in real-world applications, such as energy consumption and traffic prediction. While recent methods demonstrate good forecasting abilities, they have three fundamental limitations. (i).Discrete neural architectures:Interlacing individually parameterized spatial and temporal blocks to encode rich underlying patterns leads to discontinuous latent state trajectories and higher forecasting numerical errors. (ii).High complexity:Discrete approaches complicate models with dedicated designs and redundant parameters, leading to higher computational and memory overheads. (iii).Reliance on graph priors:Relying on predefined static graph structures limits their effectiveness and practicability in real-world applications. In this paper, we address all the above limitations by proposing a continuous model to forecastMultivariateTime series with dynamicGraph neuralOrdinaryDifferentialEquations (MTGODE). Specifically, we first abstract multivariate time series into dynamic graphs with time-evolving node features and unknown graph structures. Then, we design and solve a neural ODE to complement missing graph topologies and unify both spatial and temporal message passing, allowing deeper graph propagation and fine-grained temporal information aggregation to characterize stable and precise latent spatial-temporal dynamics. Our experiments demonstrate the superiorities ofMTGODEfrom various perspectives on five time series benchmark datasets.