Modern time series data often exhibit complex dependence and structural changes which are not easily characterised by shifts in the mean or model parameters. We propose a nonparametric data segmentation methodology for multivariate time series termed NP-MOJO. By considering joint characteristic functions between the time series and its lagged values, NP-MOJO is able to detect change points in the marginal distribution, but also those in possibly non-linear serial dependence, all without the need to pre-specify the type of changes. We show the theoretical consistency of NP-MOJO in estimating the total number and the locations of the change points, and demonstrate the good performance of NP-MOJO against a variety of change point scenarios. We further demonstrate its usefulness in applications to seismology and economic time series.

翻译：现代时间序列数据常呈现复杂依赖性和结构变化，这些特征难以通过均值或模型参数的变化来描述。我们提出一种面向多变量时间序列的非参数数据分割方法，命名为NP-MOJO。通过考虑时间序列与其滞后值之间的联合特征函数，NP-MOJO能够检测边缘分布中的变点，以及可能存在的非线性序列依赖性变点，而无需预先指定变点类型。我们证明了NP-MOJO在估计变点总数和位置时的理论一致性，并展示了该方法在多种变点场景下的优异性能。此外，我们进一步验证了该方法在地震学和经济时间序列应用中的实用性。