Changepoint detection in time series in pure python

pip install changepy

    >>> from changepy import pelt
    >>> from changepy.costs import normal_mean
    >>> size = 100

    >>> mean_a = 0.0
    >>> mean_b = 10.0
    >>> var = 0.1

    >>> data_a = np.random.normal(mean_a, var, size)
    >>> data_b = np.random.normal(mean_b, var, size)
    >>> data = np.append(data_a, data_b)

    >>> pelt(normal_mean(data, var), len(data))
    [0, 100] # since data is random, sometimes it might be different, but most of the time there will be at most a couple more values around 100

For more examples see pelt_test.py

Currently there is only one algorithm for changepoint evaluation, the PELT algorithm [1].

The PELT algorithm requires a cost function. Currently there are three functions available through this library. However, you could implement your own, for your specific needs. Those functions are:

• normal_mean, which expects normal distributed data, with changing mean
• normal_var, which expects normal distributed data, with changing variance
• normal_meanvar, which expects normal distributed data, with changing mean and variance
• poisson, which expect poisson distributed data, with changing mean
• exponential, which expect exponential distributed data, with changing mean

Test with python test_pelt.py

This is mostly a port from other libraries, most of all from STOR-i's changepoint package for julia and rkillick cpt package for r

[1]: Killick R, Fearnhead P, Eckley IA (2012) Optimal detection of changepoints with a linear computational cost, JASA 107(500), 1590-1598

MIT