We propose the Multiple Changepoint Isolation (MCI) method for detecting multiple changes in the mean and covariance of a functional process. We first introduce a pair of projections to represent the high and low frequency features of the data. We then apply total variation denoising and introduce a new regionalization procedure to split the projections into multiple regions. Denoising and regionalizing act to isolate each changepoint into its own region, so that the classical univariate CUSUM statistic can be applied region-wise to find all changepoints. Simulations show that our method accurately detects the number and locations of changepoints under many different scenarios. These include light and heavy tailed data, data with symmetric and skewed distributions, sparsely and densely sampled changepoints, and both mean and covariance changes. We show that our method outperforms a recent multiple functional changepoint detector and several univariate changepoint detectors applied to our proposed projections. We also show that the MCI is more robust than existing approaches, and scales linearly with sample size. Finally, we demonstrate our method on a large time series of water vapor mixing ratio profiles from atmospheric emitted radiance interferometer measurements.
翻译:我们提出多变点隔离法(MCI),用于检测功能过程平均值和常态的多重变化。我们首先推出一对预测,以代表数据的高频和低频特征。我们然后采用全变分分分法,并引入新的区域化程序,将预测分成多个区域。不区分和地区化行动,将每个变化点分离到自己的区域,以便传统的单变点 CUSUM 统计数据可以按区域来查找所有变化点。模拟显示,我们的方法准确地检测了许多不同情景下的变化点的数量和位置。其中包括光和重尾部数据、有对称和偏差分布的数据、零星和密集抽样变化点以及中位和共变异性的变化。我们显示,我们的方法超越了最近一个多功能变点探测器和对我们的预测应用的数个未变点探测器。我们还显示,MCI比现有方法更坚固,并且与样本大小相近的尺度。最后,我们展示了我们用于大型时间序列的大气气压阵列气压阵列图像剖面图的方法。