24 citations · 24 across the 8 of their papers we have counts for
6 papers · 1 filter
Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection
Martin Uray, Saverio Messineo, Roland Kwitt +1
Multivariate Time Series Anomaly Detection (MTSAD) is essential for reliability and safety in domains such as industrial process monitoring and financial risk management, yet conve…
Anomaly Detection for Sparse and Irregular Multivariate Time Series with Latent SDEs
Martin Uray, Dominik Geng, Florian Graf +2
Multivariate time series anomaly detection (MTSAD) is critical for a wide range of application areas, such as industrial monitoring, cybersecurity, or healthcare. Real-world data i…
Federated Learning for Multivariate Time Series Anomaly Detection in Industrial Automation
Khayyam Nosrati, Martin Uray, Saverio Messineo +2
Federated learning (FL) has broadened the horizon for multivariate time series anomaly detection (MTSAD). However, benchmarking such anomaly detection methods within FL paradigm po…
The Flood Complex: Large-Scale Persistent Homology on Millions of Points
Florian Graf, Paolo Pellizzoni, Martin Uray +2
We consider the problem of computing persistent homology (PH) for large-scale Euclidean point cloud data, aimed at downstream machine learning tasks, where the exponential growth o…
Neural Persistence Dynamics
Sebastian Zeng, Florian Graf, Martin Uray +2
We consider the problem of learning the dynamics in the topology of time-evolving point clouds, the prevalent spatiotemporal model for systems exhibiting collective behavior, such…
Topological Data Analysis in smart manufacturing: State of the art and futuredirections
Martin Uray, Barbara Giunti, Michael Kerber +1
Topological Data Analysis (TDA) is a discipline that applies algebraic topology techniques to analyze complex, multi-dimensional data. Although it is a relatively new field, TDA ha…