Efficient spatial-temporal feature aggregation for multivariate time series forecasting with STCA

Abstract

Multivariate time series (MTS) prediction plays a crucial role in many practical applications. Although spatio-temporal graph neural networks (STGNNs) have demonstrated excellent performance in MTS prediction due to the advantages of graph convolutional networks and time series modeling, their high computational complexity limits their applicability in resource constrained environments. To improve prediction accuracy while maintaining model simplicity and computational efficiency, inspired by Spatial-temporal identity (STID), this paper introduces a novel MTS prediction framework—Spatial-Temporal Channel Aggregation (STCA). This framework consists of two modules: the Channel Point Aggregation Fusion module (CPAF) enhances the capture of local spatial information and efficiently models temporal dependencies through depthwise separable convolutions and pointwise convolutions. the Selective Attention(SelAttn) module employs a self-attention mechanism to uncover complex dependencies among features. Experimental results show that STCA outperforms existing methods on multiple benchmark datasets, achieving higher prediction accuracy while significantly reducing training time.