Clustering-property Matters: A Cluster-aware Network for Large Scale Multivariate Time Series Forecasting

Large-scale Multivariate Time Series (MTS) widely exist in various real-world systems, imposing significant demands on model efficiency. A recent work, STID, addressed the high complexity issue of popular Spatial-Temporal Graph Neural Networks (STGNNs). Despite its success, when applied to large-scale MTS data, the number of parameters of STID for modeling spatial dependencies increases substantially, leading to over-parameterization issues and suboptimal performance. These observations motivate us to explore new approaches for modeling spatial dependencies in a parameter-friendly manner. In this paper, we argue that the spatial properties of variables are essentially the superposition of multiple cluster centers. Accordingly, we propose a Cluster-Aware Network (CANet), which effectively captures spatial dependencies by mining the implicit cluster centers of variables. CANet solely optimizes the cluster centers instead of the spatial information of all nodes, thereby significantly reducing the parameter amount. Extensive experiments on two large-scale datasets validate our motivation and demonstrate the superiority of CANet.