# About Anomaly Detection Thresholding

**URL:** https://discourse.numenta.org/t/about-anomaly-detection-thresholding/2088
**Category:** NuPIC
**Tags:** anomaly-detection, question
**Created:** [April 4, 2017, 8:14am UTC](https://discourse.numenta.org/t/about-anomaly-detection-thresholding/2088 "2017-04-04T08:14:18Z")
**Posts on this page:** 1
**Showing post:** 5

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### Author: ![rhyolight](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.numenta.org/rhyolight/32/3922_2.png) [@rhyolight](https://discourse.numenta.org/u/rhyolight)
#### Post date: [April 5, 2017, 3:41am UTC](https://discourse.numenta.org/t/about-anomaly-detection-thresholding/2088/5 "2017-04-05T03:41:40Z")

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Here is another resource that describes the details, but yes, I suggest you ignore the anomaly score and use the likelihood entirely.

> [@Real-Time Anomaly Detection for Streaming Analytics](https://discourse.numenta.org/t/real-time-anomaly-detection-for-streaming-analytics/1049):
>
> Subutai Ahmad, Scott Purdy (Submitted on 8 Jul 2016) Much of the world’s 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 fro…

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