anompy

anompy is a Python package of forecasting and anomaly detection algorithms.

Installation

$ pip install git+https://github.com/takuti/anompy.git

Usage

Generate dummy time-series:

>>> import random
>>> series = [random.random() for i in range(10)]
>>> series
[0.29749066250070444, 0.17992724665541393, 0.24201406949661697, 0.3467356134915024, 0.45318143064943217, 0.20825014566859423, 0.597497516445304, 0.5442072127508967, 0.1920841531842088, 0.2711214524302953]

Import BaseDetector which simply returns the last observed data point as a forecasted value, and create a detector with initial data point (i.e., training sample) and threshold:

>>> from anompy.detector.base import BaseDetector
>>> detector = BaseDetector(series[0], threshold=0.5)

Get forecasted time-series and their anomaly labels by calling detect() method:

>>> detector.detect(series[1:])
[(0.29749066250070444, False), (0.17992724665541393, False), (0.24201406949661697, False), (0.3467356134915024, False), (0.45318143064943217, False), (0.20825014566859423, False), (0.597497516445304, True), (0.5442072127508967, True), (0.1920841531842088, False)]

See this notebook for more examples.

Algorithm

anompy currently supports following algorithms:

• BaseDetector
  • Directly use the last observation as a forecasted value, and detect anomaly based on threshold.
• AverageDetector
  • Forecast either global average, simple moving average or weighted moving average.
• ExponentialSmoothing, DoubleExponentialSmoothing, and TripleExponentialSmoothing
  • See "Exponential smoothing" on Wikipedia.
  • Triple exponential smoothing is also known as Holt-Winters method.
• Experimental
  • ChangeFinder
  • SingularSpectrumTransform
  • StreamAnomalyDetector