Multi-scale Multi-step Dependency Graph Neural Network for Multivariate Time-Series Forecasting

This study addressed the limitations of existing graph neural network methods in time-series prediction, specifically the inability to establish strong dependencies between variables and the weak correlation in time-series across different time scales. To overcome these challenges, we proposed a graph neural network-based multi-scale multi-step dependency (GMSSD) model. To capture temporal dependencies in time-series data, we first designed a temporal convolution module that learns multi-scale representations between sequences. We extracted features at multiple scales using dilated convolutions and a gated linear unit (GLU) while controlling the information flow, thereby capturing temporal dependencies in time-series data. Furthermore, we employed a gated recurrent unit (GRU) and fully connected layers to derive the graph structure and capture the complex relationships between variables in the sequence data. In particular, existing graph neural network methods have a strong dependence on graph structures and are unable to adapt to complex and dynamic graph structures. They also have limitations in capturing long-range dependency relationships within the graph. Therefore, a graph convolution module is designed to explore the current node information and its neighbor information. It has the capability to integrate information contributions from different time steps, effectively capturing the spatial dependencies among nodes. The experimental results show that the proposed model outperformed existing methods in both single-step and multi-step prediction tasks. This study provided a novel approach for time-series forecasting and achieved significant improvements.