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的依赖关系与结构变化，这些变化难以通过均值或模型参数的偏移来简单刻画。我们提出一种针对多变量时间序列的非参数数据分割方法——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能够检测边际分布中的变点，也能捕捉可能存在的非线性序列依赖中的变点，且无需预先指定变化类型。我们证明了NP-MOJO在估计变点总数和位置时的理论一致性，并展示了其在多种变点场景下的优异性能。此外，我们进一步通过地震学和经济时间序列的应用案例证实了其实用价值。