Event Cartography: Latent Point Process Embeddings

Many important phenomena arise naturally as temporal point processes with different types of events influencing future events in complex ways. Estimation of multivariate point processes is a notorious proposition. We take inspiration from spatiotemporal point processes, where relationships depend only on relative distances in real Euclidean space, to suggest embedding arbitrary event types in a latent space. We demonstrate that we can simultaneously learn this embedding and a point process model to recover relationships among events.