Time-delay estimation for compound point-processes using hidden Markov models

Abstract

A new time-delay estimation algorithm for compound point-processes is presented. Compound point-processes, a generalization of temporal point-processes, describe processes with discrete events, where each occurrence time is associated with certain features. It is shown that, although the events are not observable, the time delays from events at one location to the same events at a second location can be estimated using hidden Markov models based on the associated features. We demonstrate the performance of this time-delay estimation algorithm with an application to the estimation of section-related traffic data in road traffic monitoring and control systems.