A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection

M Jin, HY Koh, Q Wen, D Zambon… - … on Pattern Analysis …, 2024 - ieeexplore.ieee.org
Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

Graph neural networks for multivariate time series regression with application to seismic data

S Bloemheuvel, J van den Hoogen, D Jozinović… - International Journal of …, 2023 - Springer
Abstract Machine learning, with its advances in deep learning has shown great potential in
analyzing time series. In many scenarios, however, additional information that can …

Pay attention to evolution: Time series forecasting with deep graph-evolution learning

G Spadon, S Hong, B Brandoli, S Matwin… - … on Pattern Analysis …, 2021 - ieeexplore.ieee.org
Time-series forecasting is one of the most active research topics in artificial intelligence. It
has the power to bring light to problems in several areas of knowledge, such as …

Spatio-temporal graph neural networks for predictive learning in urban computing: A survey

G Jin, Y Liang, Y Fang, Z Shao, J Huang… - … on Knowledge and …, 2023 - ieeexplore.ieee.org
With recent advances in sensing technologies, a myriad of spatio-temporal data has been
generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal …

Filling the g_ap_s: Multivariate time series imputation by graph neural networks

A Cini, I Marisca, C Alippi - arXiv preprint arXiv:2108.00298, 2021 - arxiv.org
Dealing with missing values and incomplete time series is a labor-intensive, tedious,
inevitable task when handling data coming from real-world applications. Effective spatio …

Graph neural networks for temporal graphs: State of the art, open challenges, and opportunities

A Longa, V Lachi, G Santin, M Bianchini, B Lepri… - arXiv preprint arXiv …, 2023 - arxiv.org
Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static)
graph-structured data. However, many real-world systems are dynamic in nature, since the …

Scalable spatiotemporal graph neural networks

A Cini, I Marisca, FM Bianchi, C Alippi - Proceedings of the AAAI …, 2023 - ojs.aaai.org
Neural forecasting of spatiotemporal time series drives both research and industrial
innovation in several relevant application domains. Graph neural networks (GNNs) are often …

Correlation-aware spatial–temporal graph learning for multivariate time-series anomaly detection

Y Zheng, HY Koh, M Jin, L Chi, KT Phan… - … on Neural Networks …, 2023 - ieeexplore.ieee.org
Multivariate time-series anomaly detection is critically important in many applications,
including retail, transportation, power grid, and water treatment plants. Existing approaches …

TAMP-S2GCNets: coupling time-aware multipersistence knowledge representation with spatio-supra graph convolutional networks for time-series forecasting

Y Chen, I Segovia-Dominguez… - International …, 2022 - openreview.net
Graph Neural Networks (GNNs) are proven to be a powerful machinery for learning complex
dependencies in multivariate spatio-temporal processes. However, most existing GNNs …

Chickenpox cases in Hungary: a benchmark dataset for spatiotemporal signal processing with graph neural networks

B Rozemberczki, P Scherer, O Kiss, R Sarkar… - arXiv preprint arXiv …, 2021 - arxiv.org
Recurrent graph convolutional neural networks are highly effective machine learning
techniques for spatiotemporal signal processing. Newly proposed graph neural network …