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.

翻译：提供无需人工干预和领域知识的在线自适应轻量级时间序列异常检测具有重要价值。过去几年中，已有多种此类异常检测方法被提出，但所有方法均仅基于单一深度学习库实现。随着深度学习库的发展，目前尚无相关评估表明不同深度学习库如何影响这些异常检测方法。随机选择深度学习库实现异常检测方法可能无法展现该方法的真实性能，也可能误导用户认为某方法优于其他方法。为此，本文通过在三款主流深度学习库中实现两种前沿异常检测方法，评估这两种方法分别受到三个深度学习库的影响程度，从而探究深度学习库对在线自适应轻量级时间序列异常检测的影响。基于四个真实开源时间序列数据集开展了一系列实验，实验结果可为选择适用于在线自适应轻量级异常检测的深度学习库提供良好参考。