<p>With the advancement of modern information technology, the collection and analysis of multi-source time series data play an important role in decision-making and process management. However, due to the complexity of capturing the dynamic features for spatio-temporal information, multi-source time series forecasting remains a challenging problem. Previous spatio-temporal models usually overlook the integration of physical and spatial dependencies between multivariable data features, as well as the effects of dynamic diffusion. To address these challenges, the authors propose a Spatio-Temporal Information Dual-layer Diffusion Network (STIDDN) for multi-source time series collaborative forecasting. STIDDN employs residual LSTM networks for temporal dependency modeling of internal features at each station, while the Dual-layer Diffusion Graph Convolutional Network (Dual-DGCN) focuses on capturing both physical and spatial dual-layer dependencies between stations, along with the dynamic diffusion process. By integrating spatio-temporal information through a skip-connection and multi-head attention mechanism, STIDDN achieves effective collaborative forecasting across multiple stations. Extensive experimental results demonstrate that the proposed model consistently outperforms advanced baseline models on two different spatio-temporal prediction task datasets.</p>