NAB
The Numenta Anomaly Benchmark (by numenta)
A3
Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-end model. Based on the activation values in the target network, the alarm network decides if the given sample is normal. Thanks to the anomaly network, our method even works in strict semi-supervised settings. Strong anomaly detection results are achieved on common data sets surpassing current baseline methods. Our semi-supervised anomaly detection method allows to inspect large amounts of data for anomalies across various applications. (by Fraunhofer-AISEC)
NAB | A3 | |
---|---|---|
3 | 1 | |
1,895 | 9 | |
0.8% | - | |
0.0 | 0.0 | |
10 months ago | almost 2 years ago | |
Jupyter Notebook | Python | |
GNU Affero General Public License v3.0 | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
NAB
Posts with mentions or reviews of NAB.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-03-15.
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Kafka Stream Processing in SigNoz - an opensource DataDog alternative
u/buzzwordd we will have an open-source anomaly detection framework that will work with SigNoz and Prometheus as a plugin. We did a small POC with some algorithms at https://github.com/numenta/NAB benchmarks and see the relevance with unsupervised and real-time training algorithms.
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Does anyone here use artificial intelligence and machine learning with their jobs?
If you need realtime training on unsupervised data, have a look at https://github.com/numenta/NAB and the benchmark solutions. I played with them some time ago and the results seemed interesting
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[P] Looking for Resources on Anomaly Detection
Also Numenta NAB paper is a good starting point. https://github.com/numenta/NAB
A3
Posts with mentions or reviews of A3.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2020-12-31.
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[P] Looking for Resources on Anomaly Detection
As the code is available as well, you can directly test the approach and see if it is a viable solution for the telemetry data: https://github.com/Fraunhofer-AISEC/A3/
What are some alternatives?
When comparing NAB and A3 you can also consider the following projects:
awesome-TS-anomaly-detection - List of tools & datasets for anomaly detection on time-series data.
alibi-detect - Algorithms for outlier, adversarial and drift detection
Netdata - The open-source observability platform everyone needs
signoz - SigNoz is an open-source observability platform native to OpenTelemetry with logs, traces and metrics in a single application. An open-source alternative to DataDog, NewRelic, etc. 🔥 🖥. 👉 Open source Application Performance Monitoring (APM) & Observability tool