Event Cartography: Latent Point Process Embeddings
CoRR(2020)
摘要
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.
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