The goal of the change-point detection is to discover changes of time series distribution. One of the state of the art approaches of change-point detection is based on direct density ratio estimation. In this work, we show how existing algorithms can be generalized using various binary classification and regression models. In particular, we show that the Gradient Boosting over Decision Trees and Neural Networks can be used for this purpose. The algorithms are tested on several synthetic and real-world datasets. The results show that the proposed methods outperform classical RuLSIF algorithm. Discussion of cases where the proposed algorithms have advantages over existing methods is also provided.

变化点检测的目的是发现时间序列分布的变化。改变点检测的最先进方法之一是基于直接密度比估计。在这项工作中，我们展示了如何使用各种二进制分类和回归模型来概括现有算法。尤其是，我们证明了基于决策树和神经网络的梯度提升可以用于此目的。该算法已在多个综合和真实数据集上进行了测试。结果表明，所提出的方法优于传统的RuLSIF算法。还讨论了所提出的算法比现有方法具有优势的情况。