EGENN: An Efficient Graph-Enhanced Neural Network for Multivariate Time Series Forecasting

Abstract

Graph Neural Network (GNN) has been widely applied in multivariate time series forecasting due to its excellent relationship modeling capabilities. However, current methods still face limitations in computational efficiency or time series expression capabilities. To address these issues, we propose an Efficient Graph-Enhanced Neural Network (EGENN), which consists of an adjacency matrix generator, GNN, and projection module. Firstly, EGENN designs a spectral similarity-based graph construction method and further enhances the expressive power of temporal features. Secondly, we introduce an inter-layer attention graph convolutional network, which adaptively aggregates information from different network depths to better capture complex patterns. Finally, a predictive projection strategy fusing wavelet convolutions and patch-wise transformation is proposed to produce compact parameterization and extended receptive fields. Experiments on five datasets from different domains show that our model achieves state-of-the-art prediction performance while maintaining low computational resource consumption.