GSA-Forecaster: Forecasting Graph-Based Time-Dependent Data with Graph Sequence Attention
This addresses forecasting challenges in applications with graph-based, time-dependent data, but it appears incremental as it builds on existing models with a new attention mechanism.
The paper tackles the problem of forecasting graph-based, time-dependent data by introducing GSA-Forecaster, a model that uses a new graph sequence attention mechanism to handle temporal dependencies, and it shows superior effectiveness compared to existing state-of-the-art models on real-world datasets.
Forecasting graph-based, time-dependent data has broad practical applications but presents challenges. Effective models must capture both spatial and temporal dependencies in the data, while also incorporating auxiliary information to enhance prediction accuracy. In this paper, we identify limitations in current state-of-the-art models regarding temporal dependency handling. To overcome this, we introduce GSA-Forecaster, a new deep learning model designed for forecasting in graph-based, time-dependent contexts. GSA-Forecaster utilizes graph sequence attention, a new attention mechanism proposed in this paper, to effectively manage temporal dependencies. GSA-Forecaster integrates the data's graph structure directly into its architecture, addressing spatial dependencies. Additionally, it incorporates auxiliary information to refine its predictions further. We validate its performance using real-world graph-based, time-dependent datasets, where it demonstrates superior effectiveness compared to existing state-of-the-art models.