Evolving Super Graph Neural Networks for Large-Scale Time-Series Forecasting

Hongjie Chen, Ryan A. Rossi, Sungchul Kim, Kanak Mahadik, Hoda Eldardiry

Abstract: Graph Recurrent Neural Networks (GRNN) excel in time-series prediction by modeling complicated non-linear relationships among time-series. However, most GRNN models target small datasets that only have tens of time-series or hundreds of time-series. Therefore, they fail to handle large-scale datasets that have tens of thousands of time-series, which exist in many real-world scenarios. To address this scalability issue, we propose Evolving Super Graph Neural Networks (ESGNN), which target large-scale datasets and significantly boost model training. Our ESGNN models multivariate time-series based on super graphs, where each super node is associated with a set of time-series that are highly correlated with each other. To further precisely model dynamic relationships between time-series, ESGNN quickly updates super graphs on the fly by using the LSH algorithm to construct the super edges. The embeddings of super nodes are learned through end-to-end learning and are then used with each target time-series for forecasting. Experimental result shows that ESGNN outperforms previous state-of-the-art methods with a significant runtime speedup (\(3{\times }\)–\(40{\times }\) faster) and space-saving (\(5{\times }\)–\(4600{\times }\) less), while only sacrificing little or negligible prediction accuracy. An ablation study is also conducted to investigate the effectiveness of the number of super nodes and the graph update interval.