# ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series

arXiv: https://arxiv.org/abs/2004.02319 · HTML: https://mlsanity.com/paper/2004.02319/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2020.

## Built on by (most cited first, 9 of 9)

- [A Survey on Anomaly Detection for Technical Systems using LSTM Networks](https://mlsanity.com/paper/2105.13810/) (2021)
- [How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?](https://mlsanity.com/paper/2102.06560/) (2021)
- [SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Series](https://mlsanity.com/paper/2104.09968/) (2021)
- [RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series](https://mlsanity.com/paper/2303.00409/) (2023)
- [RoLA: A Real-Time Online Lightweight Anomaly Detection System for Multivariate Time Series](https://mlsanity.com/paper/2305.16509/) (2023)
- [NP-Free: A Real-Time Normalization-free and Parameter-tuning-free Representation Approach for Open-ended Time Series](https://mlsanity.com/paper/2304.06168/) (2023)
- [Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series Anomaly Detection](https://mlsanity.com/paper/2305.00595/) (2023)
- [Impact of Recurrent Neural Networks and Deep Learning Frameworks on Real-time Lightweight Time Series Anomaly Detection](https://mlsanity.com/paper/2407.18439/) (2024)
- [AI Learning Algorithms: Deep Learning, Hybrid Models, and Large-Scale Model Integration](https://mlsanity.com/paper/2410.09186/) (2024)

## Abstract (arXiv)

Anomaly detection is an active research topic in many different fields such as intrusion detection, network monitoring, system health monitoring, IoT healthcare, etc. However, many existing anomaly detection approaches require either human intervention or domain knowledge, and may suffer from high computation complexity, consequently hindering their applicability in real-world scenarios. Therefore, a lightweight and ready-to-go approach that is able to detect anomalies in real-time is highly sought-after. Such an approach could be easily and immediately applied to perform time series anomaly detection on any commodity machine. The approach could provide timely anomaly alerts and by that enable appropriate countermeasures to be undertaken as early as possible. With these goals in mind, this paper introduces ReRe, which is a Real-time Ready-to-go proactive Anomaly Detection algorithm for streaming time series. ReRe employs two lightweight Long Short-Term Memory (LSTM) models to predict and jointly determine whether or not an upcoming data point is anomalous based on short-term historical data points and two long-term self-adaptive thresholds. Experiments based on real-world time-series datasets demonstrate the good performance of ReRe in real-time anomaly detection without requiring human intervention or domain knowledge.