7 citations · 13 across the 8 of their papers we have counts for
6 papers · 1 filter
Stationarity-Aware Retrieval-Augmented Time Series Forecasting
Shiqiao Zhou, Holger Schöner, Zipeng Wu +3
Time series forecasting relies on historical patterns, but real-world series often exhibit non-stationarity and regime shifts that challenge fully parametric forecasters. Inspired…
Adaptive Bernstein Change Detector for High-Dimensional Data Streams
Marco Heyden, Edouard Fouché, Vadim Arzamasov +3
Change detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring and prediction systems to react, e.g., b…
Budgeted Multi-Armed Bandits with Asymmetric Confidence Intervals
Marco Heyden, Vadim Arzamasov, Edouard Fouché +1
We study the stochastic Budgeted Multi-Armed Bandit (MAB) problem, where a player chooses from arms with unknown expected rewards and costs. The goal is to maximize the total r…
Maximum Mean Discrepancy on Exponential Windows for Online Change Detection
Florian Kalinke, Marco Heyden, Georg Gntuni +2
Detecting changes is of fundamental importance when analyzing data streams and has many applications, e.g., in predictive maintenance, fraud detection, or medicine. A principled ap…
Efficient Subspace Search in Data Streams
Edouard Fouché, Florian Kalinke, Klemens Böhm
In the real world, data streams are ubiquitous -- think of network traffic or sensor data. Mining patterns, e.g., outliers or clusters, from such data must take place in real time.…
Monte Carlo Dependency Estimation
Edouard Fouché, Klemens Böhm
Estimating the dependency of variables is a fundamental task in data analysis. Identifying the relevant attributes in databases leads to better data understanding and also improves…