A minimal and pure python fork of @nicolasmiller's library pyculiarity. That is a Python port of Twitter's AnomalyDetection R Package. The original source and examples are available here.

This fork is focused on python3 compatibility and no dependency on R. This is done using statsmodel's young tsa.seasonal_decompose, which differs in output from the Loess STL implementation used by the original pyculiarity library. The results are not identical as a result, but are pretty close.

I've also stripped out some unused/unimplimented code to try to make this a little more readable/understandable. That part is a work in progress. Part of that includes more intuitive handling of the timestamps, just have a 'timestamp' column with unix timestamps in it.

The original library is on pypi as pyculiarity, so to not clash with that, I've uploaded this as pyculiar. It will still install the libarary as pyculiarity so this should function as a drop in replacement.

pip install pyculiar

As in Twitter's package, there are two top level functions, one for time-series data and one for simple vector processing, detect_ts and detect_vec respectively. The first one expects a two-column Pandas DataFrame consisting of timestamps and values. The second expects either a single-column DataFrame or a Series.

Here's an example of loading Twitter's example data (included in the tests directory) with Pandas and passing it to Pyculiarity for processing.

from pyculiarity import detect_ts
import matplotlib.pyplot as plt
import pandas as pd
import matplotlib
matplotlib.style.use('ggplot')

__author__ = 'willmcginnis'

if __name__ == '__main__':
    # first run the models
    twitter_example_data = pd.read_csv('../tests/raw_data.csv', usecols=['timestamp', 'count'])
    results = detect_ts(twitter_example_data, max_anoms=0.05, alpha=0.001, direction='both', only_last=None)

    # format the twitter data nicely
    twitter_example_data['timestamp'] = pd.to_datetime(twitter_example_data['timestamp'])
    twitter_example_data.set_index('timestamp', drop=True)

    # make a nice plot
    f, ax = plt.subplots(2, 1, sharex=True)
    ax[0].plot(twitter_example_data['timestamp'], twitter_example_data['count'], 'b')
    ax[0].plot(results['anoms'].index, results['anoms']['anoms'], 'ro')
    ax[0].set_title('Detected Anomalies')
    ax[1].set_xlabel('Time Stamp')
    ax[0].set_ylabel('Count')
    ax[1].plot(results['anoms'].index, results['anoms']['anoms'], 'b')
    ax[1].set_ylabel('Anomaly Magnitude')
    plt.show()

Which will give the plot:

The tests are run with nose as follows:

nosetests .

Changes Copyright 2016 Will McGinnis Python port Copyright 2015 Nicolas Steven Miller Original R source Copyright 2015 Twitter, Inc and other contributors

Licensed under the GPLv3