Work

Forecasting Graph-Based Time-Dependent Data with Graph Sequence Attention

Year: 2025

Type: article

Abstract: 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 article, 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 article, 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.

Source: ACM Transactions on Knowledge Discovery from Data

DOI: 10.1145/3721435

Authors: Yang Li, Di Wang, José M. F. Moura

Institutions: Iowa State University, Carnegie Mellon University, Microsoft (United States)

Language: English

Topic: Advanced Graph Neural Networks

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4408126100

FWCI:

Cites: 22

Cited by:

Related to: 10