  Providing online adaptive lightweight time series anomaly detection without
human intervention and domain knowledge is highly valuable. Several such
anomaly detection approaches have been introduced in the past years, but all of
them were only implemented in one deep learning library. With the development
of deep learning libraries, it is unclear how different deep learning libraries
impact these anomaly detection approaches since there is no such evaluation
available. Randomly choosing a deep learning library to implement an anomaly
detection approach might not be able to show the true performance of the
approach. It might also mislead users in believing one approach is better than
another. Therefore, in this paper, we investigate the impact of deep learning
libraries on online adaptive lightweight time series anomaly detection by
implementing two state-of-the-art anomaly detection approaches in three
well-known deep learning libraries and evaluating how these two approaches are
individually affected by the three deep learning libraries. A series of
experiments based on four real-world open-source time series datasets were
conducted. The results provide a good reference to select an appropriate deep
learning library for online adaptive lightweight anomaly detection.
