Metadata-Version: 2.1
Name: cpdetect
Version: 0.0.2
Summary: A package containing multiple change-point detection methods for normal mean model (mean shift detection).
Home-page: https://github.com/Szymex49/cpdetect
Author: Szymon Malec
Author-email: szymon.malec@o2.pl
License: GPLv3
Description: # Change-point detection with cpdetect
        
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        **cpdetect** is a python package designed for change point-detection using statistical methods. This is a first version and considers only one change-point model which is normal mean model. This assumes normally distributed time series values and changes only in the mean (mean shift). The package offers three detection methods which are Binary Segmentation (BS), Backward Detection (BWD) and Screening and Ranking algorithm (SaRa).
        
        
        ## Install
        
        To install the package use
        
            pip install cpdetect
        
        
        ## Example usage
        
        Let's import some useful libraries first.
        ```python
        import scipy.stats as sp
        import numpy as np
        from matplotlib import pyplot as plt
        ```
        
        Now we can create an example time series with three change-points.
        ```python
        # mean shifts between 0 and 3, sigma = 2
        Y1 = sp.norm.rvs(0, 2, 200)
        Y2 = sp.norm.rvs(3, 2, 200)
        Y3 = sp.norm.rvs(0, 2, 200)
        Y4 = sp.norm.rvs(3, 2, 200)
        Y = np.hstack((Y1, Y2, Y3, Y4))
        plt.plot(Y)
        ```
        <img src="./images/mean_shift_example.jpg" width="500">
        
        To find the change-points location we can use `BinSeg` which contains binary segmentation implementation.
        ```python
        from cpdetect import BinSeg
        
        bs = BinSeg()                 # creating object
        
        bs.fit(Y, stat='Z', sigma=2)  # fitting to data
        
        plt.plot(bs.stat_values)      # statistic plot
        
        bs.predict(0.01)              # change-point detection
        ```
        <img src="./images/bs_Z_plot.jpg" width="500">
        
        If we don't know what the standard deviation (`sigma`) is, we can use T statistic.
        ```python
        bs.fit(Y, stat='T')        # fitting to data
        
        plt.plot(bs.stat_values)   # statistic plot
        
        bs.predict(0.01)           # change-point detection
        ```
        <img src="./images/bs_T_plot.jpg" width="500">
        
        If we don't know the distribution of time series values, we can't use normal mean models. Then we can use bootstrap which finds the statistic distribution by itself.
        ```python
        bs.predict(0.01, bootstrap_samples=1000)
        ```
        
        
        
        ## Libraries used
        - `numpy`
        - `pandas`
        - `scipy`
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
