Multi-level Relational Learning with Synergistic Graphs for Multivariate Time Series Forecasting

Yang Xie, Qiao Liu, Rui Hou, Tingting Dai, Tian Lan

Keywords: Multivariate time series forecasting，Multi-level relational learning，Graph neural network，Hypergraph neural network

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Abstract:

Multivariate Time Series (MTS) forecasting involves analyzing the evolution and interrelationships of multiple variables over time.
Effectively mining relationships between MTS variables remains challenging as variables may imply multiple relational patterns. Recently, graph-based approaches have exhibited substantial effectiveness in capturing relationships between MTS variables. However, these methods often adhere to the paradigm of capturing low-level pairwise relationships, which limits their ability to capture other high-level beyond pair-wise relational patterns. To address this issue, we present a synergistic graph learning framework that combines the modeling advantages of graphs and hypergraphs to uncover more comprehensive relational patterns. This framework mainly consists of two parts. Firstly, we introduced a Synergistic Relation Construction module, which incorporates dynamic graph and hypergraph structures to model low-level pairwise and high-level beyond pairwise relationships among variables, representing multi-level relational patterns through obtained adjacency matrices and incidence metrics. Additionally, we developed a Synergistic Relation Learning mechanism, that leverages novel synergistic graph and hypergraph convolutional networks to facilitate spatial dependency interactions across multi-levels, along with temporal convolutional networks to capture more comprehensive spatial-temporal dependencies. We conducted comprehensive experiments on four benchmark datasets, and experimental results demonstrate that our model outperforms the state-of-the-art performance. The source code will be available online.

Url Link To Your Supplementary Code: https://github.com/YoungXiee/MSG

Primary Area: Deep Learning (architectures, deep reinforcement learning, generative models, deep learning theory, etc.)

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Submission Number: 66