Time series changepoint detection

Related tags

Machine Learning timeseries changepoint pelt
Overview

changepy

Changepoint detection in time series in pure python

Install

pip install changepy

Examples

    >>> 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

Reference

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

Other implementations

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

License

MIT

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Comments
  • cost function of normal mean

    cost function of normal mean

    I am writing to ask about the cost function of your change point detection algorithm. I compared the performance of the pelt function of changepy and cpt function of changepoint in R. Using pelt(normalmean(mydata, var), len(mydata)) and cpt.mean(tmp_data,penalty = "SIC",method="PELT") and find they has different result. Taking a closer look of the code I find there are difference between the cost function of mean.norm in changepoint package and normal_mean in changepy. The cost function of normal_mean requires a external input of variance which I think is just the variance of the whole data. This variance act like a constant to be divided each time the cost is computed, which is the point I don't understand. Shouldn't the the variance computed separately for different segment position as designed in the changepoint package in R. I am not sure whether this is the cause of the difference in result and that's why I write to ask you about it. Could you provide any insight regarding this part?

    opened by JinnyCC 3
  • Poisson Implementation

    Poisson Implementation

    Would you consider adding a cost function for the Poisson distribution? I believe it's just the single lambda parameter. A lot of time series data tends to be count data, so this would be very broadly useful. Thanks in advance!

    opened by hyvenglaven 3
  • something not working

    something not working

    I tried forking this repo and adding a meanvar procedure, but it did not work. I went back and tried to produce an elbow plot for one of the other procedures (var), but that had an erratic shape. I think there is something wrong with the core of this program. It should be A/B tested against the R package to really figure out if it is functioning.

    opened by jrhaberstroh 2
Owner
Rui Gil
Rui Gil
GitHub
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