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 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.

我们提出了一种新技术，用于对不规则间隔时间序列结构中的变化点的数量和位置进行一致的估计。分割过程的核心是集成二进制分割方法（EBS），该技术是将大量使用二进制分割方法的多个变化点检测任务应用于不同长度数据的子样本，然后结果相结合以创建总体答案。我们不限制时间序列可以具有的变更点总数，因此，当变更点之间的间隔较短时，我们提出的方法效果很好。我们主要的变化点检测统计数据是随时间变化的自回归条件持续时间模型，在该模型上我们应用了转换过程以对其进行解相关。为了检查EBS的性能，我们提供了针对各种类型场景的模拟研究。还提供了一致性证明。我们的方法在R包中实现eNchange，可从CRAN下载。