Multivariate Time Series forecasting based on temporal decomposition and graph neural network

It is quite challenging to forecast the Multivariate Time Series (MTS) accurately due to the high dimensionality of MTS and the entangled correlation between variables. Recently, graph-based networks have been demonstrated to be an effective model to handle the complex correlations between MTS. However, all existing graph-based methods construct the graph model of the MTS using only the shallow correlations from the raw MTS data, ignoring the deep-rooted correlations hidden in the features. In this paper, we propose for the first time to construct a comprehensive graph model of MTS that incorporates both shallow correlations from raw data and hidden correlations from decomposed temporal properties. Then, we propose a novel graph-based MTS forecasting framework, which optimizes the graph structure jointly with the model parameters. By doing so, the graph structure can adaptively model the correlations of MTS at a deep level, while the joint optimization can make the constructed graph compatible with the forecasting tasks of MTS, contributing to a globally optimal solution. Finally, we conduct extensive experiments on seven real-world datasets, the results demonstrate the superiority of our method on MTS forecasting over the state-of-the-art baselines. The source codes of the experiments with datasets are available at https: //github. com/ironweng/MF-TDGNN.

Authors

• Yan Qiao
• Pei Zhao
• Junjie Wang School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen International Research Institute for Artificial Intelligence, Harbin Institute of Technology, Shenzhen
• Rongyao Hu School of Computer Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
• Minyue Li
• Xinyu Yuan
• Meng Li
• Zhenchun Wei
• Cuiying Feng

Keywords

• Multivariate Time Series
• Time series forecasting
• Graph neural networks
• Temporal decomposition