# RePAD2: Real-Time, Lightweight, and Adaptive Anomaly Detection for Open-Ended Time Series

arXiv: https://arxiv.org/abs/2303.00409 · HTML: https://mlsanity.com/paper/2303.00409/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 1 Mar 2023.

## Builds on (most cited first, 10 of 10)

- [Time-Series Anomaly Detection Service at Microsoft](https://mlsanity.com/paper/1906.03821/) (2019)
- [Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmark](https://mlsanity.com/paper/1510.03336/) (2015)
- [Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network](https://mlsanity.com/paper/1703.09752/) (2017)
- [Automatic Anomaly Detection in the Cloud Via Statistical Learning](https://mlsanity.com/paper/1704.07706/) (2017)
- [RePAD: Real-time Proactive Anomaly Detection for Time Series](https://mlsanity.com/paper/2001.08922/) (2020)
- [ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series](https://mlsanity.com/paper/2004.02319/) (2020)
- [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)
- [Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection](https://mlsanity.com/paper/1801.03168/) (2018)
- [Intrusion Detection using Continuous Time Bayesian Networks](https://mlsanity.com/paper/1401.3851/) (2014)

## Built on by (most cited first, 6 of 6)

- [RoLA: A Real-Time Online Lightweight Anomaly Detection System for Multivariate Time Series](https://mlsanity.com/paper/2305.16509/) (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)
- [Evaluation of k-means time series clustering based on z-normalization and NP-Free](https://mlsanity.com/paper/2401.15773/) (2024)
- [GAD: A Real-time Gait Anomaly Detection System with Online Adaptive Learning](https://mlsanity.com/paper/2405.09561/) (2024)
- [Exploring the impact of Optimised Hyperparameters on Bi-LSTM-based Contextual Anomaly Detector](https://mlsanity.com/paper/2501.15053/) (2025)

## Abstract (arXiv)

An open-ended time series refers to a series of data points indexed in time order without an end. Such a time series can be found everywhere due to the prevalence of Internet of Things. Providing lightweight and real-time anomaly detection for open-ended time series is highly desirable to industry and organizations since it allows immediate response and avoids potential financial loss. In the last few years, several real-time time series anomaly detection approaches have been introduced. However, they might exhaust system resources when they are applied to open-ended time series for a long time. To address this issue, in this paper we propose RePAD2, a lightweight real-time anomaly detection approach for open-ended time series by improving its predecessor RePAD, which is one of the state-of-the-art anomaly detection approaches. We conducted a series of experiments to compare RePAD2 with RePAD and another similar detection approach based on real-world time series datasets, and demonstrated that RePAD2 can address the mentioned resource exhaustion issue while offering comparable detection accuracy and slightly less time consumption.