Welcome. This repository contains the data and scripts comprising the Numenta Anomaly Benchmark (NAB). NAB is a novel benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial timeseries data files plus a novel scoring mechanism designed for real-time applications.
Included are the tools to allow you to easily run NAB on your own anomaly detection algorithms; see the NAB entry points info. Competitive results tied to open source code will be posted in the wiki on the Scoreboard. Let us know about your work by emailing us at nab@numenta.org or submitting a pull request.
This readme is a brief overview and contains details for setting up NAB. Please refer to the NAB publication or the NAB Whitepaper in the wiki for more details about NAB scoring, data, motivation, etc.
We encourage you to publish your results on running NAB, and share them with us at nab@numenta.org. Please cite the following publication when referring to NAB:
Lavin, Alexander and Ahmad, Subutai. "Evaluating Real-time Anomaly Detection Algorithms – the Numenta Anomaly Benchmark", Fourteenth International Conference on Machine Learning and Applications, December 2015. [PDF]
The NAB scores are normalized such that the maximum possible is 100.0 (i.e. the perfect detector), and a baseline of 0.0 is determined by the "null" detector (which makes no detections).
| Detector | Standard Profile | Reward Low FP | Reward Low FN |
|---|---|---|---|
| Perfect | 100.0 | 100.0 | 100.0 |
| Numenta HTM* | 65.3 | 58.6 | 69.4 |
| Twitter ADVec v1.0.0 | 47.1 | 33.6 | 53.5 |
| Etsy Skyline | 35.7 | 27.1 | 44.5 |
| Bayesian Changepoint** | 17.7 | 3.2 | 32.2 |
| Sliding Threshold | 15.0 | -26.2 | 30.1 |
| Random*** | 11.0 | 1.2 | 19.5 |
| Null | 0.0 | 0.0 | 0.0 |
As of NAB v1.0
* The results correspond to NuPIC and nupic.core SHAs 42f701d and c030b84 respectively, but the latest version of NuPIC should still work (the results may not be identical).
** The original algorithm was not designed for anomaly detection. Details of the implementation and parameter tuning are in the detector's code.
*** Scores reflect the mean across a range of random seeds. 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.
Please see the wiki section on contributing algorithms for discussion on posting algorithms to the scoreboard.
The NAB corpus of 58 timeseries data files is designed to provide data for research in streaming anomaly detection. It is comprised of both real-world and artifical timeseries data containing labeled anomalous periods of behavior.
The majority of the data is real-world from a variety of sources such as AWS server metrics, Twitter volume, advertisement clicking metrics, traffic data, and more. All data is included in the repository, with more details in the data readme. We are in the process of adding more data, and actively searching for more data. Please contact us at nab@numenta.org if you have similar data (ideally with known anomalies) that you would like to see incorporated into NAB.
The NAB version will be updated whenever new data (and corresponding labels) is added to the corpus; NAB is currently in v1.0.
For comparison, here are the NAB V1.0 scores for some additional flavors of HTM.
NumentaTM HTM detector uses the implementation of temporal memory found
here.
Numenta HTM detector with no likelihood uses the raw anomaly scores directly. To
run without likelihood, set the variable self.useLikelihood in
numenta_detector.py
to False.
| Detector |Standard Profile | Reward Low FP | Reward Low FN | |---------------|---------|------------------|---------------|---------------| | Numenta HTM* | 65.3 | 58.6 | 69.4 | | NumentaTM HTM* | 61.2 | 52.4 | 66.1 | | Numenta HTM*, no likelihood | 52.52 | 41.09 | 58.25 |
* The results correspond to NuPIC and nupic.core SHAs 42f701d and c030b84 respectively, but the latest version of NuPIC should still work (the results may not be identical).
- OSX 10.9 and higher
- Amazon Linux (via AMI)
Other platforms may work but have not been tested.
You need to manually install the following:
- Python 2.7
- pip
- NumPy
- NuPIC (only required if running the Numenta detector)
Use the Github links provided in the right sidebar.
cd NAB
(sudo) pip install -r requirements.txt
This will install the additional required modules pandas and simplejson.
Recommended:
python setup.py install --user
Or if you are actively working on the code and are familiar with manual PYTHONPATH setup:
python setup.py develop --prefix=/some/other/path/
There are several different use cases for NAB:
-
If you just want to look at all the results we reported in the paper, there is no need to run anything. All the data files are in the data subdirectory and all individual detections for reported algorithms are checked in to the results subdirectory. Please see the README files in those locations.
-
If you have your own algorithm and want to run the NAB benchmark, please see the NAB Entry Points section in the wiki. (The easiest option is often to simply run your algorithm on the data and output results in the CSV format we specify. Then run the NAB scoring algorithm to compute the final scores. This is how we scored the Twitter algorithm, which is written in R.)
-
If you are a NuPIC user and just want to run the Numenta HTM detector follow the directions below to "Run HTM with NAB".
-
If you want to run everything including the bundled Skyline detector follow the directions below to "Run full NAB". Note that this will take hours as the Skyline code is quite slow.
First make sure NuPIC is installed and working properly. Then:
cd /path/to/nab
python run.py -d numenta --detect --score --normalize
This will run the Numenta detector only and produce normalized scores. Note that by default it tries to use all the cores on your machine. The above command should take about 20-30 minutes on a current powerful laptop with 4-8 cores. For debugging you can run subsets of the data files by modifying and specifying specific label files. Please type:
python run.py --help
to see all the options.
Note that to replicate results exactly as in the paper you may need to checkout the specific version of NuPIC (and associated nupic.core) that is noted in the Scoreboard:
cd /path/to/nupic/
git checkout -b nab {TAG NAME}
cd /path/to/nupic.core/
git checkout -b nab {TAG NAME}
cd /path/to/nab
python run.py
This will run everything and produce results files for the anomaly detection methods. Included in the repo are the Numenta anomaly detection method, as well as methods from the Etsy Skyline anomaly detection library, a random detector, and a null detector. This will also pass those results files to the scoring script to generate final NAB scores. Note: this option will take many many hours to run.
The run.py command has a number of useful options. To view a description of the command line options please enter
python run.py --help