Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
Anomaly detection is an essential task for many firms and organizations. Identifying unusual patterns in messy multivariate time series can prevent catastrophic events and optimize operations. Traditional statistical methods struggle with high-dimensional data and complex temporal dependencies. In this paper, we propose a novel approach, combining Generative Adversarial Networks (GANs), a reconstruction-based framework, and Graph Neural Networks (GNNs) for effective and interpretable anomaly detection. Our method involves representing multivariate times series as graphs and training two interconnected GANs and an Autoencoder to capture the normal behavior of the networks. Anomalies are detected by measuring the reconstruction error and the discriminator's score. Our case study on the signals of the INFN CNAF Tier-1 data center demonstrates the effectiveness of our approach in terms of robustness and interpretability. We validate our model on NASA's public space telemetry signals, a popular multivariate time series benchmark and observe cutting-edge performance. This work highlights the potential of GNNs in developing interpretable deep learning solutions for real-world applications.
Recommended Citation
Cornali, Roberto; Ronchieri, Elisabetta; Cesini, Daniele; and Dell’Agnello, Luca, "Graph Neural Network Solutions for Anomaly Detection in Time Series" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 3.
https://aisel.aisnet.org/hicss-58/da/sensors/3
Graph Neural Network Solutions for Anomaly Detection in Time Series
Hilton Waikoloa Village, Hawaii
Anomaly detection is an essential task for many firms and organizations. Identifying unusual patterns in messy multivariate time series can prevent catastrophic events and optimize operations. Traditional statistical methods struggle with high-dimensional data and complex temporal dependencies. In this paper, we propose a novel approach, combining Generative Adversarial Networks (GANs), a reconstruction-based framework, and Graph Neural Networks (GNNs) for effective and interpretable anomaly detection. Our method involves representing multivariate times series as graphs and training two interconnected GANs and an Autoencoder to capture the normal behavior of the networks. Anomalies are detected by measuring the reconstruction error and the discriminator's score. Our case study on the signals of the INFN CNAF Tier-1 data center demonstrates the effectiveness of our approach in terms of robustness and interpretability. We validate our model on NASA's public space telemetry signals, a popular multivariate time series benchmark and observe cutting-edge performance. This work highlights the potential of GNNs in developing interpretable deep learning solutions for real-world applications.
https://aisel.aisnet.org/hicss-58/da/sensors/3