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conda-forge.org : r-changeforest
Change point detection aims to identify structural breaks in the probability distribution of a time series. Existing methods either assume a parametric model for within-segment distributions or are based on ranks or distances and thus fail in scenarios with a reasonably large dimensionality. `changeforest` implements a classifier-based algorithm that consistently estimates change points without any parametric assumptions, even in high-dimensional scenarios. It uses the out-of-bag probability predictions of a random forest to construct a classifier log-likelihood ratio that gets optimized using a computationally feasible two-step method. See [1] for details. [1] M. Londschien, P. Bühlmann, and S. Kovács (2023). "Random Forests for Change Point Detection" Journal of Machine Learning Research
Registry
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Source
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purl: pkg:conda/r-changeforest
Keywords: change-point-detection, changepoint, changepoint-detection, signal-processing
License: BSD-3-Clause
Latest release: about 2 years ago
First release: over 2 years ago
Stars: 17 on GitHub
Forks: 3 on GitHub
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Last synced: 1 day ago