论文标题
随机森林用于变化点检测
Random Forests for Change Point Detection
论文作者
论文摘要
我们提出了一种使用分类器的新型多元非参数多元变更点检测方法。我们构建了一个分类器日志类比率，该比例使用类概率预测来比较不同的变更点配置。我们提出了一种计算上可行的搜索方法，该方法特别适合随机森林，并用thrangeforest表示。但是，该方法可以与任何产生类概率预测的分类器配对，我们还通过使用k-neareb-neight邻居分类器来说明这一点。我们证明，当与一致的分类器配对时，它始终将变更点定位在单个变更点设置中。与现有的多元非参数变化点检测方法相比，我们提出的方法更改方面的经验性能在广泛的模拟研究中提高了经验性能。我们的方法的有效实现可用于R，Python和Rust用户在ChangeForest软件包中。
We propose a novel multivariate nonparametric multiple change point detection method using classifiers. We construct a classifier log-likelihood ratio that uses class probability predictions to compare different change point configurations. We propose a computationally feasible search method that is particularly well suited for random forests, denoted by changeforest. However, the method can be paired with any classifier that yields class probability predictions, which we illustrate by also using a k-nearest neighbor classifier. We prove that it consistently locates change points in single change point settings when paired with a consistent classifier. Our proposed method changeforest achieves improved empirical performance in an extensive simulation study compared to existing multivariate nonparametric change point detection methods. An efficient implementation of our method is made available for R, Python, and Rust users in the changeforest software package.
