218 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Universität UlmDE13 papers
- Esslingen University of Applied SciencesDE5 papers
- Leiden UniversityNL5 papers
- Karlsruhe Institute of TechnologyDE4 papers
- Saarland UniversityDE4 papers
- University of StuttgartDE4 papers
- University of TübingenDE4 papers
- Forschungszentrum JülichDE3 papers
- RheinMain University of Applied SciencesDE3 papers
- University of Applied Sciences LeidenNL3 papers
- University of GöttingenDE3 papers
- University of SiegenDE3 papers
10 papers · 1 filter
Surprised by Attention: Predictable Query Dynamics for Time Series Anomaly Detection
Kadir-Kaan Özer, René Ebeling, Markus Enzweiler
Multivariate time series anomalies often manifest as shifts in cross-channel dependencies rather than simple amplitude excursions. In autonomous driving, for instance, a steering c…
STREAM-VAE: Dual-Path Routing for Slow and Fast Dynamics in Vehicle Telemetry Anomaly Detection
Kadir-Kaan Özer, René Ebeling, Markus Enzweiler
Automotive telemetry data exhibits slow drifts and fast spikes, often within the same sequence, making reliable anomaly detection challenging. Standard reconstruction-based methods…
DQS: A Low-Budget Query Strategy for Enhancing Unsupervised Data-driven Anomaly Detection Approaches
Lucas Correia, Jan-Christoph Goos, Thomas Bäck +1
Truly unsupervised approaches for time series anomaly detection are rare in the literature. Those that exist suffer from a poorly set threshold, which hampers detection performance…
Decision-Focused Fine-Tuning of Time Series Foundation Models for Dispatchable Feeder Optimization
Maximilian Beichter, Nils Friederich, Janik Pinter +7
Time series foundation models provide a universal solution for generating forecasts to support optimization problems in energy systems. Those foundation models are typically traine…
PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time Series
Lucas Correia, Jan-Christoph Goos, Thomas Bäck +1
Benchmarking anomaly detection approaches for multivariate time series is a challenging task due to a lack of high-quality datasets. Current publicly available datasets are too sma…
Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions
Lucas Correia, Jan-Christoph Goos, Philipp Klein +2
Time-series anomaly detection plays an important role in engineering processes, like development, manufacturing and other operations involving dynamic systems. These processes can…
TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series Data
Lucas Correia, Jan-Christoph Goos, Philipp Klein +2
As attention to recorded data grows in the realm of automotive testing and manual evaluation reaches its limits, there is a growing need for automatic online anomaly detection. Thi…
MA-VAE: Multi-head Attention-based Variational Autoencoder Approach for Anomaly Detection in Multivariate Time-series Applied to Automotive Endurance Powertrain Testing
Lucas Correia, Jan-Christoph Goos, Philipp Klein +2
A clear need for automatic anomaly detection applied to automotive testing has emerged as more and more attention is paid to the data recorded and manual evaluation by humans reach…
Predicting the Time Until a Vehicle Changes the Lane Using LSTM-based Recurrent Neural Networks
Florian Wirthmüller, Marvin Klimke, Julian Schlechtriemen +2
To plan safe and comfortable trajectories for automated vehicles on highways, accurate predictions of traffic situations are needed. So far, a lot of research effort has been spent…
Active Output Selection Strategies for Multiple Learning Regression Models
Adrian Prochaska, Julien Pillas, Bernard Bäker
Active learning shows promise to decrease test bench time for model-based drivability calibration. This paper presents a new strategy for active output selection, which suits the n…