# RePAD: Real-time Proactive Anomaly Detection for Time Series

arXiv: https://arxiv.org/abs/2001.08922 · HTML: https://mlsanity.com/paper/2001.08922/

## Key facts

- Cited by 11 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 24 Jan 2020.

## Built on by (most cited first, 11 of 11)

- [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)
- [Fraud Analytics: A Decade of Research -- Organizing Challenges and Solutions in the Field](https://mlsanity.com/paper/2212.04329/) (2022)
- [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)
- [Evaluation of k-means time series clustering based on z-normalization and NP-Free](https://mlsanity.com/paper/2401.15773/) (2024)
- [Mutual Information Surprise: Rethinking Unexpectedness in Autonomous Systems](https://mlsanity.com/paper/2508.17403/) (2025)
- [Real-Time Proactive Anomaly Detection via Forward and Backward Forecast Modeling](https://mlsanity.com/paper/2602.11539/) (2026)

## Abstract (arXiv)

During the past decade, many anomaly detection approaches have been introduced in different fields such as network monitoring, fraud detection, and intrusion detection. However, they require understanding of data pattern and often need a long off-line period to build a model or network for the target data. Providing real-time and proactive anomaly detection for streaming time series without human intervention and domain knowledge is highly valuable since it greatly reduces human effort and enables appropriate countermeasures to be undertaken before a disastrous damage, failure, or other harmful event occurs. However, this issue has not been well studied yet. To address it, this paper proposes RePAD, which is a Real-time Proactive Anomaly Detection algorithm for streaming time series based on Long Short-Term Memory (LSTM). RePAD utilizes short-term historic data points to predict and determine whether or not the upcoming data point is a sign that an anomaly is likely to happen in the near future. By dynamically adjusting the detection threshold over time, RePAD is able to tolerate minor pattern change in time series and detect anomalies either proactively or on time. Experiments based on two time series datasets collected from the Numenta Anomaly Benchmark demonstrate that RePAD is able to proactively detect anomalies and provide early warnings in real time without human intervention and domain knowledge.