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