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：现代时间序列数据通常呈现复杂的依赖关系和结构变化，这些变化不易通过均值或模型参数的偏移来表征。我们提出了一种名为NP-MOJO的多变量时间序列非参数数据分割方法。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，也能检测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方面的理论一致性，并展示了NP-MOJO在多种变点场景下的优异性能。我们进一步通过将其应用于地震学和经济学时间序列数据，证明了其实用价值。