  Real-time lightweight time series anomaly detection has become increasingly
crucial in cybersecurity and many other domains. Its ability to adapt to
unforeseen pattern changes and swiftly identify anomalies enables prompt
responses and critical decision-making. While several such anomaly detection
approaches have been introduced in recent years, they primarily utilize a
single type of recurrent neural networks (RNNs) and have been implemented in
only one deep learning framework. It is unclear how the use of different types
of RNNs available in various deep learning frameworks affects the performance
of these anomaly detection approaches due to the absence of comprehensive
evaluations. Arbitrarily choosing a RNN variant and a deep learning framework
to implement an anomaly detection approach may not reflect its true performance
and could potentially mislead users into favoring one approach over another. In
this paper, we aim to study the influence of various types of RNNs available in
popular deep learning frameworks on real-time lightweight time series anomaly
detection. We reviewed several state-of-the-art approaches and implemented a
representative anomaly detection approach using well-known RNN variants
supported by three widely recognized deep learning frameworks. A comprehensive
evaluation is then conducted to analyze the performance of each implementation
across real-world, open-source time series datasets. The evaluation results
provide valuable guidance for selecting the appropriate RNN variant and deep
learning framework for real-time, lightweight time series anomaly detection.
