#Anomaly 2 benchmark results install#

You need to manually install the following:

#Anomaly 2 benchmark results windows 10#

NAB has been tested on Windows 10 but is not † Algorithm was an entry to the 2016 NAB Competition.

* From NuPIC version 0.5.6 ( available on PyPI). Run without likelihood, set the variable eLikelihood in

Numenta HTM detector with no likelihood uses the raw anomaly scores directly.

NumentaTM HTM detector uses the implementation of temporal memory found.

Please see Felix Andrew's blog post on experiments with this algorithm.

nab-comportex is a twist on HTM anomaly detection using Comportex, a community-driven HTM implementation in Clojure.

HTM Java is a Community-Driven Java port of HTM.

If you are interested in replicating the results shown in the paper, use this version.

Numenta HTM using NuPIC v.0.5.6: This version of NuPIC was used to generate the data for the paper mentioned above (Unsupervised real-time anomaly detection for streaming data.

Additional Scoresįor comparison, here are the NAB V1.0 scores for some additional flavors of HTM. Is added to the corpus or other significant changes are made. The NAB version will be updated whenever new data (and corresponding labels) All data is included in the repository, with more details in theĭata (ideally with known anomalies) that you would like to see incorporated Server metrics, Twitter volume, advertisement clicking metrics, traffic data,Īnd more. The majority of the data is real-world from a variety of sources such as AWS It is comprised of both real-worldĪnd artifical timeseries data containing labeled anomalous periods of The NAB corpus of 58 timeseries data files is designed to provide data for Please see the wiki section on contributingįor discussion on posting algorithms to the scoreboard.

#Anomaly 2 benchmark results code#

**** We have included the results for RCF using an AWS proprietary implementation even though the algorithm code is not open source, the algorithm description is public and the code we used to run NAB on RCF is open source. The spread of scores for each profile are 7.95 to 16.83 for Standard, -1.56 to 2.14 for Reward Low FP, and 11.34 to 23.68 for Reward Low FN. *** Scores reflect the mean across a range of random seeds. Implementation details are in the detector's code. ** The original algorithm was modified for anomaly detection. * From NuPIC version 1.0 ( available on PyPI) the range in scores represents runs using different random seeds. the perfect detector), and a baseline of 0.0 is determined by the 'null' detector (which makes no detections). The NAB scores are normalized such that the maximum possible is 100.0 (i.e. Unsupervised real-timeĪnomaly detection for streaming data. We encourage you to publish your results on running NAB, and share them withĪhmad, S., Lavin, A., Purdy, S., & Agha, Z. Evaluating Real-time Anomaly Detection Algorithms - Original publication of NAB.Unsupervised real-time anomaly detection for streaming data - The main paper, covering NAB and Numenta's HTM-based anomaly detection algorithm.Please refer to the following for more details about NAB scoring, data, and This readme is a brief overview and contains details for setting up NAB. Results tied to open source code will be posted on the Included are the tools to allow you to run NAB on your own anomaly detectionĪlgorithms see the NAB entry points info.

#Anomaly 2 benchmark results plus#

Plus a novel scoring mechanism designed for real-time applications. It isĬomposed of over 50 labeled real-world and artificial timeseries data files NAB is a novel benchmark for evaluatingĪlgorithms for anomaly detection in streaming, real-time applications. This repository contains the data and scripts which comprise the