Event Cartography: Latent Point Process Embeddings

05/05/2020
by   Myrl G. Marmarelis, et al.
10

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/03/2018

Communicating Concurrent Processes

Process algebra CSP only permits a process to engage in one event on a m...
research
02/17/2022

Variational Neural Temporal Point Process

A temporal point process is a stochastic process that predicts which typ...
research
06/20/2023

Deep graph kernel point processes

Point process models are widely used to analyze asynchronous events occu...
research
02/11/2021

Mutually exciting point process graphs for modelling dynamic networks

A new class of models for dynamic networks is proposed, called mutually ...
research
08/11/2022

Learning Point Processes using Recurrent Graph Network

We present a novel Recurrent Graph Network (RGN) approach for predicting...
research
11/06/2019

weg2vec: Event embedding for temporal networks

Network embedding techniques are powerful to capture structural regulari...
research
09/16/2019

Dirichlet Depths for Point Process

Statistical depths have been well studied for multivariate and functiona...

Please sign up or login with your details

Forgot password? Click here to reset