roufcp - Rough Fuzzy Changepoint Detection

Gradual Change-Point Detection Library based on Rough Fuzzy Changepoint Detection algorithm roufcp.

The package is available in PyPI.

Usage

>> import numpy as np
>> from roufcp import roufCP
>> X = np.concatenate([np.ones(20) * 5, np.zeros(20), np.ones(20) * 10]) + np.random.randn(60)
>> roufCP(delta = 3, w = 3).fit(X, moving_window = 10, k = 2)

Try help(roufCP) for detailed documentation.

roufCP is a class for Rough Fuzzy Changepoint Detection with the following attributes and functions.

• Attributes

  • delta : int, The fuzzyness parameter, typically between 5-100
  • w : int, The roughness parameter, typically between 5-100

• Methods

  • fit_from_regularity_measure(X, regularity_measure, k) : fit the data X with help of the regularity measure and output the estimated changepoints

  • fit(X, moving_window, method, k): fit the data X with given regularity measures and output the estimated changepoints. The method argument defaults to kstest, available options are;

    • meandiff : Two sample mean difference
    • ttest : Two sample t test statistic
    • kstest : Two sample Kolmogorov test statistic
    • mannwhitney : Two sample Mann Whitney U statistic
    • anderson-darling : Two sample Anderson Darling test statistic
    • adf : Augmented Dickey Fuller test of stationarity with linear trend
    • kpss : Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test of stationarity with linear trend

  • hypothesis_test(cp_list, cp_entropy, mu, sigma, a_delta): Performs hypothesis testing of the null hypothesis that there is no changepoint in the data, against the alternative that there is changepoint at the specified indices, and outputs the p-value

Authors & Contributors

• Subhrajyoty Roy - https://subroy13.github.io/
• Ritwik Bhaduri - https://github.com/Ritwik-Bhaduri
• Sankar Kumar Pal - https://www.isical.ac.in/~sankar/

License

This code is licensed under MIT License.