Prof. Dr. Felix Naumann

Efficient Subsequence Anomaly Detection On Time Series Data

in coorporation with Rolls Royce

We are investigating algorithms, that can detect anomalous subsequences on time series. An anomaly can stem from different events, such as errors in sensors or constructions, diseases, or special events. Many challenges arise in the process of detecting anomalies:

  • Length of a time series
  • Width of a time series
  • Anomaly contribution of different dimensions
  • Generalization to different data characteristics


  • Sebastian Schmidl, Phillip Wenig, Thorsten Papenbrock: HYPEX: Hyperparameter Optimization in Time Series Anomaly Detection. Proceedings of the conference on Database Systems for Business, Technology, and Web (BTW), 2023 (to appear)
  • Phillip Wenig, Sebastian Schmidl, Thorsten Papenbrock: TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms. PVLDB 12:(15), 2022
    [Paper]  [Project Page]  [DOI:10.14778/3554821.3554873]
  • Sebastian Schmidl, Phillip Wenig, Thorsten Papenbrock: Anomaly Detection in Time Series: A Comprehensive Evaluation. PVLDB 9:(15), 2022
    [Paper]  [Poster]  [Project Page]  [DOI:10.14778/3538598.3538602]
  • Johannes Schneider, Phillip Wenig, Thorsten Papenbrock: Distributed detection of sequential anomalies in univariate time series. The VLDB Journal (2021)
    [Paper]  [Poster]  [Project Page]  [DOI:10.1007/s00778-021-00657-6]