Metadata-Version: 2.1
Name: ChangePointDetectorEVT
Version: 0.0.8
Summary: This module takes a time series and returns: (a) the underlaying linear trend and (b) the times where there is a change in the trend
Home-page: https://github.com/mhaupt63/ChangePointDetector
Author: Michael Hauptman
Author-email: michael.hauptman1@gmail.com
License: UNKNOWN
Project-URL: Bug Tracker, https://github.com/mhaupt63/ChangePointDetectorissues
Keywords: ['Time Series','Anomaly','Detection','Kalman','EVT']
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE.txt

ChangePointDetector

This module returns change points in a time series, using Kalman filters and EVT as described in https://www.robots.ox.ac.uk/~sjrob/Pubs/LeeRoberts_EVT.pdf

1. Prepare your time series as data plus Panda dates
2. Create  the necessary Kalman representation by creating a "session" object by calling the ChangePoint class, e.g.:
	Session=ChangePointDetector.ChangePointDetectorSession(data,dates). 'SeasonalityPeriods' is an optional input, e.g 12 = calendar month seasonality
4. Determine the changepoints by running the ChangePointDetectorFunction on your "session", e.g. Results=Session.ChangePointDetectorFunction()
   This will return a "Results" object that contains the following:
	- ChangePoints. This is a list of 0s and 1s the length of the data, where 1s represent changepoints
	- Prediction. This is the Kalman smoothed actuals, plus a 3 period forecast. Note no forecast will be made if there is a changepoint in the last 3 			dates
	- PredictionVariance.  Variance of the smoothed actuals and forecast
	- ExtendedDates. These are the original dates plus 3 exta for the forecast (if a forecast has been made)
	- Trend. This is the linear change factor
	- TrendVariance. Variance of the trend

