It is quite common that the structure of a time series changes abruptly. Identifying these change points and describing the model structure in the segments between these change points is of interest. In this paper, time series data is modelled assuming each segment is an autoregressive time series with possibly different autoregressive parameters. This is achieved using two main steps. The first step is to use a likelihood ratio scan based estimation technique to identify these potential change points to segment the time series. Once these potential change points are identified, modified parametric spectral discrimination tests are used to validate the proposed segments. A numerical study is conducted to demonstrate the performance of the proposed method across various scenarios and compared against other contemporary techniques.

中文翻译：

---

自回归时间序列数据中的多变点检测与验证

时间序列的结构突然变化是很常见的。识别这些变化点并描述这些变化点之间的段中的模型结构是有意义的。在本文中，时间序列数据被建模，假设每个段是一个自回归时间序列，可能具有不同的自回归参数。这是通过两个主要步骤实现的。第一步是使用基于似然比扫描的估计技术来识别这些潜在的变化点来分割时间序列。一旦确定了这些潜在的变化点，就可以使用修改后的参数光谱鉴别测试来验证建议的段。进行了数值研究，以证明所提出的方法在各种情况下的性能，并与其他当代技术进行比较。