From the 1 of 7 linked papers with an AI index.
7 papers
Exploring Zero-Shot Foundation Models for Multivariate Time Series Anomaly Detection
Martin Uray, Saverio Messineo, Roland Kwitt +1
The paper investigates using a pre‑trained univariate forecasting foundation model (TimesFM) in a zero‑shot manner for multivariate time‑series anomaly detection on the SWaT benchm…
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…
Topologically Stable Hough Transform
Stefan Huber, Kristóf Huszár, Michael Kerber +1
We propose an alternative formulation of the well-known Hough transform to detect lines in point clouds. Replacing the discretized voting scheme of the classical Hough transform by…
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…
Persistence-based Hough Transform for Line Detection
Johannes Ferner, Stefan Huber, Saverio Messineo +2
The Hough transform is a popular and classical technique in computer vision for the detection of lines (or more general objects). It maps a pixel into a dual space -- the Hough spa…