This repository contains the data and scripts necessary to replicate the results in the forthcoming Numenta Anomaly Benchmark (NAB) paper.

We hope you will compare these results with your own anomaly detection methods. Competitive results tied to open source code will be listed here. Let us know about your work by submitting a pull request.

The NAB corpus of timeseries datasets is designed to provide data for research in streaming anomaly detection. It is comprised of both artificial and real-world timeseries data containing labeled anomalous periods of behavior.

All data are ordered, timestamped, single-valued metrics collected at 5 minute intervals.

Much of the real-world data are values from AWS server metrics as collected by the [AmazonCloudwatch service](https://aws.amazon .com/documentation/cloudwatch/). Example metrics include CPU Utilization, Network Bytes In, and Disk Read Bytes. There are also real world sensor readings from some large machines.

All data is included in the repository. We are actively searching for more data

Detect anomalous behavior in streaming data in real-time and provide useful alerts.

You must be able to handle streaming data.

Post-hoc analysis is insufficient for this task. All classifications must be done as if the data is being presented for the first time, in real time.

You must be able to provide a response in a reasonable amount of time.

This benchmark is representative of a task in human time-scales. Per-record classification should take place in less than 5 minutes. Anomaly detection should happen as quickly as possible following the onset of an anomaly.

You must minimize the cost of using your detection technique.

It is insufficient to just catch all anomalies. A high false-positive rate will reduce or eliminate an institution's willingness to use your technique.

• 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 (Source Required)

It is also assumed you have a full checkout and working version of NuPIC by this point.

cd ~/
git clone https://github.com/numenta/NAB.git

cd NAB
(sudo) pip install -r requirements.txt

This will install a bunch of requirements, such as pandas and simplejson.

cd /path/to/nab
python setup.py develop (for now)
python run.py

This will produce results files for the Numenta anomaly detection method as well as baseline results using methods from the Etsy Skyline anomaly detection library. This will also pass those results files to the analyze_results.py script to generate final scores.

Once NAB is finalized (not yet!) to replicate results exactly you will need a specific version of NuPIC:

cd /path/to/nupic/
git checkout -b nab {TAG NAME}

Then follow build directions in the NuPIC README.md.

This is the file format for each datafile in the corpus.

• All files MUST be in CSV format
• All files MUST have exactly one header row
• The header row MUST have the following fields
  • "timestamp"
    • The time of the end of the metric collection window
    • e.g 2014-04-01 12:00:00.000000 is values collected between
      • 2014-04-01 11:55:00.000000
      • 2014-04-01 12:00:00.000000
  • "value"
    • The collected metric, e.g. CPUUtilization percent
  • "label"
    • This is ground truth for the class of a record.
• Header rows MAY have other fields as well. These are ignored.
• Values in the "timestamp" column MUST follow this format
  • YYYY-MM-DD HH:MM:SS.s
  • e.g. 2014-04-01 00:00:00.000000
• Metric values in the "value" column MUST be either
  • floats
  • integers (these will be converted to floats internally)
• Values in the "label" column MUST be either
  • 0 - This record is known to be non-anomalous
  • 0.5 - This record's class is ambiguous
  • 1 - This record is known to be anomalous
• Each record MUST represent an equal amount of time
• Records MUST be in chronological order
• Records MUST be continuous such that there are no missing time steps

There is currently a very crude data visualizer available. To use it do the following:

First generate the list of data files and result files:

ls -1 */*/*.csv | grep data > data_file_paths.txt 
ls -1 */*/*/*.csv | grep results | grep -v test_results > results_file_paths.txt

From the NAB directory, type:

python -m SimpleHTTPServer 12345

Then, open Chrome and type this into the url window:

localhost:12345/nab_visualizer.html

To view data, click on "look at data", click in query window and enter “return”. This should show all the data files. You can also type in some letters into the query window and it will filter for filenames that contain those characters.

To view result files, click on "look at results" first.