Summary of A Systematic Literature Review Of Spatio-temporal Graph Neural Network Models For Time Series Forecasting and Classification, by Flavio Corradini et al.
A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification
by Flavio Corradini, Marco Gori, Carlo Lucheroni, Marco Piangerelli, Martina Zannotti
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Data Analysis, Statistics and Probability (physics.data-an)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This systematic literature review presents a comprehensive overview of spatio-temporal graph neural networks (GNNs) for time series classification and forecasting. The authors examine over 150 journal papers to identify various modeling approaches and application domains. The review provides a detailed comparison of proposed models, including links to related source code, available datasets, benchmark models, and fitting results. This information is intended to assist researchers in future studies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Spatio-temporal graph neural networks (GNNs) are used for time series analysis. Researchers have been studying these GNNs because they can capture dependencies among variables and across time points. This review looks at many papers about GNNs and what they’re used for. It compares different models and where they’re used. The review also talks about the challenges of using GNNs, like making sure results are comparable and easy to understand. |
Keywords
» Artificial intelligence » Classification » Time series
