# Manufacturing Test Results Anomalies within specs to Prevent Potential Field Failures

**URL:** https://discourse.numenta.org/t/manufacturing-test-results-anomalies-within-specs-to-prevent-potential-field-failures/5060
**Category:** Lounge
**Created:** [December 13, 2018, 5:56pm UTC](https://discourse.numenta.org/t/manufacturing-test-results-anomalies-within-specs-to-prevent-potential-field-failures/5060 "2018-12-13T17:56:50Z")
**Posts on this page:** 1
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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: [December 14, 2018, 7:51pm UTC](https://discourse.numenta.org/t/manufacturing-test-results-anomalies-within-specs-to-prevent-potential-field-failures/5060/2 "2018-12-14T19:51:04Z")

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We have a paper about it:

> [@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…

It is important that your contain contains temporal sequences. There must be some trend over time to find in the data.

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