Wasserstein-based identification of metastable states in time series data via change point detection and segment clustering
通过 Wasserstein 变点检测与分段聚类识别时间序列亚稳态
Change point detection for time series analysis is a difficult and important problem in applied statistics, for which a variety of approaches have been developed in the past several decades. Here, the Wasserstein metric is employed as a tool for change-point identification in multi-dimensional time series data in order to identify clusters in time series in an unsupervised way. We leverage the simplicity of the optimal transport cost in the 1-dimensional setting to quickly…