ROBUST CHANGEPOINT DETECTION BASED ON MULTIVARIATE RANK STATISTICS

We introduce a novel statistical test for unsupervised detection of changepoints in multidimensional sequences of temporal observations. The test statistic is based on a multivariate generalization of the Mann-Whitney Wilcoxon two-sample test. The proposed test performs nonparametric changepoint localization and returns a quantifiable measure of significance in the form of a p-value. This approach is also parameter-free and can easily be extended to cases where the data is partly censored or has missing values. The performance of the method is illustrated through experiments on a publicly available econometric datasets.