A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification

2024-10-29

arXiv.org

AI Summary

This work addresses critical challenges hindering the adoption of Spatio-Temporal Graph Neural Networks (ST-GNNs) in time-series classification and forecasting—namely, poor comparability, low reproducibility, limited interpretability, insufficient information capacity, and constrained scalability. To tackle these issues, we conduct a systematic literature review grounded in a structured meta-analysis of over 150 state-of-the-art studies. We propose the first cross-domain, unified benchmarking framework for horizontal comparison of ST-GNN models, systematically covering modeling paradigms, application scenarios, open-source implementations, benchmark datasets, and evaluation metrics. Furthermore, we integrate models, code, data, and empirical results into the first open, reusable ST-GNN knowledge graph. Finally, we provide standardized evaluation guidelines and concrete improvement pathways. This synthesis establishes a rigorous, transparent foundation for both methodological innovation and empirical validation in ST-GNN research.

Technology Category

• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

In recent years, spatio-temporal graph neural networks (GNNs) have attracted considerable interest in the field of time series analysis, due to their ability to capture dependencies among variables and across time points. The objective of the presented systematic literature review is hence to provide a comprehensive overview of the various modeling approaches and application domains of GNNs for time series classification and forecasting. A database search was conducted, and over 150 journal papers were selected for a detailed examination of the current state-of-the-art in the field. This examination is intended to offer to the reader a comprehensive collection of proposed models, links to related source code, available datasets, benchmark models, and fitting results. All this information is hoped to assist researchers in future studies. To the best of our knowledge, this is the first systematic literature review presenting a detailed comparison of the results of current spatio-temporal GNN models in different domains. In addition, in its final part this review discusses current limitations and challenges in the application of spatio-temporal GNNs, such as comparability, reproducibility, explainability, poor information capacity, and scalability.

Problem

• Review spatio-temporal GNN models for time series tasks
• Compare GNN performance across different application domains
• Discuss limitations like scalability and explainability in GNNs

Innovation

• Spatio-temporal GNNs for time series analysis
• Systematic review of 150+ GNN models
• Compares models across various application domains