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


  • Phillip Wenig, Sebastian Schmidl, Thorsten Papenbrock: TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms. Proceedings of the VLDB Endowment (PVLDB), 2022 (to appear)
    [Project Page] 
  • Sebastian Schmidl, Phillip Wenig, Thorsten Papenbrock: Anomaly Detection in Time Series: A Comprehensive Evaluation. Proceedings of the VLDB Endowment (PVLDB), 2022
    [Paper]  [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]