A Multivariate Hawkes process with Gaps in Observations

Friday, May 5, 2017 - 1:25pm - 2:25pm
Lind 305
Triet Le (National Geospatial Intelligence Agency)
Given a collection of entities (or nodes) in a network and our intermittent observations
of activities from each entity, an important problem is to learn the hidden edges depicting directional relationships among these entities. Here, we study causal relationships (excitations) that are realized by a multivariate Hawkes process. The multivariate Hawkes process (MHP) and its variations (spatio-temporal point processes) have been used to study contagion in earthquakes, crimes, neural spiking
activities, the stock and foreign exchange markets, etc. In this talk, we consider the multivariate Hawkes process with gaps in observations (MHPG). We propose a variational problem for detecting sparsely hidden that takes into account the gaps from each entity. We bypass the problem of dealing with a large amount of missing events by introducing a small number of unknown boundary