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Numenta Anomaly Benchmark (NAB)

Numenta Anomaly Benchmark (NAB) youtube video screenshot
Video: Numenta Anomaly Benchmark (NAB) (02:23)

The First Benchmark For Evaluating Anomaly Detection In Streaming Data

The Internet of Things has produced a world that’s overflowing with streaming data. As these data sources continue to grow, so does the need for anomaly detection. Uncovering anomalies allows you to:

  • Detect potential machine failures
  • Recognize changes in Twitter activity
  • Identify unexpected traffic patterns

There are different methods of anomaly detection in streaming data, but how do you measure their effectiveness? NAB is the first benchmark designed for time-series data that gives credit to finding anomalies earlier and adjusting to changed patterns.


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Real-World Dataset

NAB contains a dataset with real-world, labeled data files across multiple domains. We’ve accumulated this valuable data from years of working with customers to address their anomaly problems.

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Scoring Mechanism

We have developed a unique scoring function that rewards early detection, penalizes late or false results, and gives credit for on-line learning.

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Open Source Code Library

NAB is a modular, open source code base. Numenta will be working to build a community around NAB to add data files and test additional algorithms.


Evaluating Real-Time Anomaly Detection youtube video screenshot
Video: Evaluating Real-Time Anomaly Detection (19:23)

Evaluating Real-Time Anomaly Detection: The Numenta Anomaly Benchmark (NAB)

Subutai Ahmad, VP Research presenting NAB and discussing the need for evaluating real-time anomaly detection algorithms. This presentation was delivered at MLConf (Machine Learning Conference) in San Francisco 2015.

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NAB White Paper chart figure

White Paper: The Numenta Anomaly Benchmark (NAB)

Why did we create this benchmark? Why is anomaly detection so hard in streaming data? This paper answers those questions and highlights how business managers can use NAB to ensure they’re getting valuable insights as early as possible.

Read Whitepaper
NAB Scoreboard

Research Paper: Evaluating Real-time Anomaly Detection Algorithms – the Numenta Anomaly Benchmark (NAB)

This peer-reviewed paper was accepted to the IEEE Conference on Machine Learning and Applications December 9-11, 2015 in Miami. It contains technical details on NAB, including the mathematical explanation of the scoring system.

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NAB Github Repo

NAB Repository

This open source library contains all data files, algorithms and documentation. Use this repository to try NAB for yourself. Test your own techniques against the published algorithms and share your results.

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NAB Datasheet

Data Sheet: Numenta Anomaly Benchmark (NAB)

Download this two-page data sheet to learn more about the key components of NAB.

Download Datasheet

Try NAB for Yourself

NAB Chart Detail

Try the Numenta Anomaly Benchmark (NAB)

We’ve made it easy for you to try NAB. Visit the repository to test your own techniques and share your results. Use NAB to select the best algorithm for your specific application.

Contribute Your Data

We are committed to adding more real-world data files to our benchmark dataset. Do you have streaming data files with known anomalies? Contact us at to see if we can incorporate your data into a future version of NAB.