Flow prediction via adaptive dynamic graph with spatio-temporal correlations

Flow prediction has shown its significance in optimizing and smoothing resource allocation. However, it still faces challenges due to inherent temporal variability and dynamic spatial correlations of flow data. Although many methods have been introduced to solve the problem, they often rely on predefined graphs to capture shared patterns, which is likely to overlook the critical spatiotemporal dependencies that are necessary for mid-term and long-term prediction tasks. To address the issue, a novel flow prediction model, termed Dynamic Graph Multi-Step Prediction via Long Short-Term Memory Network and Adaptive Learning (DMLSA), is proposed. DMLSA employs Node Adaptive Learning (NAL) to learn node-specific patterns without relying on predefined static adjacency matrices. Moreover, DMLSA utilizes Graph Adaptive Generation (GAG) to extract dynamic features from node attributes and automatically infer interdependencies among flow sequences, thus enhancing the ability to predict complex flow dynamics. Additionally, DMLSA integrates multi-head self-attention mechanisms with gated recurrent units, effectively capturing both long-term and short-term node dependencies, therefore allowing for more precise identification of spatiotemporal patterns at the individual node level. Extensive experiments on five datasets demonstrate that DMLSA outperformed existing baseline models in terms of accuracy and stability for multi-step prediction tasks. •Capture spatial correlations in graphs as well as short-term and long-term time dependencies.•Propose node-adaptive learning that alleviates computational burden and enhances efficiency.•Devise graph adaptive generation that improves the ability to identify spatial relations.•Conduct multi-step prediction via identifying node-specific spatio-temporal patterns.