Graph neural networks for multivariate time series regression with application to seismic data
Open accessAbstract
Machine learning, with its advances in deep learning has shown great potential in analyzing time series. In many scenarios, however, additional information that can potentially improve the predictions is available. This is crucial for data that arise from e. g., sensor networks that contain information about sensor locations. Then, such spatial information can be exploited by modeling it via graph structures, along with the sequential (time series) information. Recent advances in adapting deep learning to graphs have shown potential in various tasks. However, these methods have not been adapted for time series tasks to a great extent. Most attempts have essentially consolidated around time series forecasting with small sequence lengths. Generally, these architectures are not well suited for regression or classification tasks where the value to be predicted is not strictly depending on the most recent values, but rather on the whole length of the time series. We propose TISER-GCN, a novel graph neural network architecture for processing, in particular, these long time series in a multivariate regression task. Our proposed model is tested on two seismic datasets containing earthquake waveforms, where the goal is to predict maximum intensity measurements of ground shaking at each seismic station. Our findings demonstrate promising results of our approach—with an average MSE reduction of 16.3%—compared to the best performing baselines. In addition, our approach matches the baseline scores by needing only half the input size. The results are discussed in depth with an additional ablation study.
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Spatial GNN models assume that the underlying structure of the data sources from which the MTS dataset is generated is known.
“Spatial GNN models (e.g., refs.[66][67][68]) are based on the assumption that the underlying structure of the data sources from which the MTS dataset is generated is known.”
From TVGeAN: Tensor Visibility Graph-Enhanced Attention Network for Versatile Multivariant Time Series Learning Tasks (2024) · cites this paper as [66]
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Self-citationLehmann, F., Gatti, F., Bertin, M., Grenié, D., & Clouteau, D. (2024) · European Journal of Environmental and Civil engineering · doi:10.1080/19648189.2024.2393883 · Cited by 1 · see citing works
Application of GGNN inference propagation model for martial art intensity evaluation
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Theoretical and practical data science and analytics: challenges and solutions
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- Authors
- Bloemheuvel, S., van den Hoogen, J., Jozinović, D., Michelini, A., & Atzmueller, M.
- Affiliations
- Tilburg University (NL)
- Roma Tre University (IT)
- Istituto Nazionale di Geofisica e Vulcanologia (IT)
- Osnabrück University (DE)
- Corresponding author
- Bloemheuvel, Stefan
- Venue
- International Journal of Data Science and Analytics
- Published
- 2022-08-30
- Volume
- 16
- Issue
- 3
- Pages
- 317-332
- ISSN
- 2364-415X (ISSN-L)
- 2364-4168
- OpenAlex
- W4293702644
- Cited by
- 75
- References
- 38
- Citations by year
- 2026: 17 · 2025: 24 · 2024: 23 · 2023: 11
- FWCI
- 6.69
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- International Journal of Data Science and Analytics (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Interreg (NWE729)
- Ministero dell’Università e della Ricerca (CUP D53J190001700)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Engineering › Building and Construction › Traffic Prediction and Management Techniques
- Keywords
- graph neural networks, long-term time series, seismic data, earthquake waveforms, seismic stations, geospatial data, sensor networks
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