Sequential Monitoring for Object-Valued Time Series

Thomas M. Menino Convention & Exhibition Center 

In this work, we propose a new procedure for monitoring change points in the marginal distribution of object-valued time series. Our approach extends a recently developed offline change-point detection method to the online setting. The proposed monitoring procedure is free of tuning parameters, can be computed recursively, and demonstrates favorable finite-sample size and power properties. We also provide theoretical justification and present numerical results based on simulated data to illustrate the effectiveness of the method.

Change-point detection

Non-Euclidean data

Object-valued data

Online monitoring