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TraceBench: Controlled Evaluation of LLM Agents for Time-Series Root-Cause Attribution
Tommaso Bendinelli, Artur Dox, Christian Holz
LLM agents are increasingly applied to anomaly detection and root-cause analysis in time-series observations collected from real-world systems; however, their performance on these…
Temporal Cardiovascular Dynamics for Improved PPG-Based Heart Rate Estimation
Berken Utku Demirel, Christian Holz
The oscillations of the human heart rate are inherently complex and non-linear -- they are best described by mathematical chaos, and they present a challenge when applied to the pr…
Learning Without Augmenting: Unsupervised Time Series Representation Learning via Frame Projections
Berken Utku Demirel, Christian Holz
Self-supervised learning (SSL) has emerged as a powerful paradigm for learning representations without labeled data. Most SSL approaches rely on strong, well-established, handcraft…
Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets
Tommaso Bendinelli, Artur Dox, Christian Holz
High-quality, error-free datasets are a key ingredient in building reliable, accurate, and unbiased machine learning (ML) models. However, real world datasets often suffer from err…
Shifting the Paradigm: A Diffeomorphism Between Time Series Data Manifolds for Achieving Shift-Invariancy in Deep Learning
Berken Utku Demirel, Christian Holz
Deep learning models lack shift invariance, making them sensitive to input shifts that cause changes in output. While recent techniques seek to address this for images, our finding…
An Unsupervised Approach for Periodic Source Detection in Time Series
Berken Utku Demirel, Christian Holz
Detection of periodic patterns of interest within noisy time series data plays a critical role in various tasks, spanning from health monitoring to behavior analysis. Existing lear…