论文标题
用于不规则间隔的数据的合奏二进制分割，带更改点
Ensemble Binary Segmentation for irregularly spaced data with change-points
论文作者
论文摘要
我们提出了一种新技术，以一致地估算不规则间隔时间序列结构中变更点的数量和位置。分割过程的核心是集合二进制分割方法（EBS），该技术使用二进制分割（BS）方法应用了大量的多个更改点检测任务（BS）方法，以不同长度的数据的子样本进行，然后将结果组合起来以创建一个整体答案。我们不限制一个时间序列可以具有的变更点的总数，因此，当变更点之间的间隔很短时，我们提出的方法效果很好。我们的主要更改点检测统计量是随着时间变化的自回归条件持续时间模型，我们在其上应用转换过程以将其解变。为了检查EB的性能，我们为各种场景提供了模拟研究。还提供了一致性证明。我们的方法是在R软件包enchange中实现的，可从Cran下载。
We propose a new technique for consistent estimation of the number and locations of the change-points in the structure of an irregularly spaced time series. The core of the segmentation procedure is the Ensemble Binary Segmentation method (EBS), a technique in which a large number of multiple change-point detection tasks using the Binary Segmentation (BS) method are applied on sub-samples of the data of differing lengths, and then the results are combined to create an overall answer. We do not restrict the total number of change-points a time series can have, therefore, our proposed method works well when the spacings between change-points are short. Our main change-point detection statistic is the time-varying Autoregressive Conditional Duration model on which we apply a transformation process in order to decorrelate it. To examine the performance of EBS we provide a simulation study for various types of scenarios. A proof of consistency is also provided. Our methodology is implemented in the R package eNchange, available to download from CRAN.
