# Real-Time Anomaly Detection for Streaming Analytics

arXiv: https://arxiv.org/abs/1607.02480 · HTML: https://mlsanity.com/paper/1607.02480/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2016.

## Built on by (most cited first, 5 of 5)

- [Effective Restoration of Source Knowledge in Continual Test Time Adaptation](https://mlsanity.com/paper/2311.04991/) (2023)
- [Real-Time Outlier Detection with Dynamic Process Limits](https://mlsanity.com/paper/2301.13527/) (2023)
- [DeCorus: Hierarchical Multivariate Anomaly Detection at Cloud-Scale](https://mlsanity.com/paper/2202.06892/) (2022)
- [Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters](https://mlsanity.com/paper/2301.00014/) (2022)
- [Learn Faster and Remember More: Balancing Exploration and Exploitation for Continual Test-time Adaptation](https://mlsanity.com/paper/2508.12643/) (2025)

## Abstract (arXiv)

Much of the worlds data is streaming, time-series data, where anomalies give significant information in critical situations. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, and learn while simultaneously making predictions. We present a novel anomaly detection technique based on an on-line sequence memory algorithm called Hierarchical Temporal Memory (HTM). We show results from a live application that detects anomalies in financial metrics in real-time. We also test the algorithm on NAB, a published benchmark for real-time anomaly detection, where our algorithm achieves best-in-class results.