Classifier based non-parametric change point detection

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Classifier based non-parametric change point detection

Change point detection tries to identify times when the probability distribution of a stochastic process or time series changes. Existing methods either assume a parametric model for within-segment distributions or a based on ranks or distances, and thus fail in scenarios with reasonably large dimensionality.

changeforest implements a classifier based algorithm that consistently estimates change points without any parametric assumptions even in high-dimensional scenarios. See [1] for details.

References

[1] M. Londschien, S. Kovács and P. Bühlmann (2021), "Random Forests and other nonparametric classifiers for multivariate change point detection", working paper.

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  • License: BSD License
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