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：在无需人工干预和领域知识的前提下实现在线自适应轻量级时间序列异常检测具有极高的应用价值。过去数年间，业界已提出多种此类异常检测方法，但所有方法均仅基于单一深度学习库实现。随着深度学习库的持续发展，不同深度学习库对这些异常检测方法的影响尚不明确——目前缺乏相关的系统性评估。若随机选择深度学习库来实现异常检测方法，可能无法展现该方法的真实性能，甚至可能误导使用者误判不同方法的优劣。为此，本文通过三种主流深度学习库实现两种先进异常检测方法，系统评估不同深度学习库对这两种方法的独立影响，深入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作用机制。基于四个真实开源时间序列数据集开展的系列实验表明，研究结果为在线自适应轻量级异常检测的深度学习库选型提供了重要参考依据。