论文标题
基于直接密度比估计的时间序列数据中变更点检测的概括
Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio Estimation
论文作者
论文摘要
更改点检测的目的是发现时间序列分布的变化。更改点检测的最新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将现有算法推广。特别是，我们表明，可以将对决策树和神经网络的梯度提升用于此目的。该算法在几个合成和现实世界数据集上进行了测试。结果表明，所提出的方法的表现优于经典的算法算法。还提供了与现有方法具有优势的案例讨论。
The goal of the change-point detection is to discover changes of time series distribution. One of the state of the art approaches of the change-point detection are 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 are also provided.
