META TEMPORAL POINT PROCESSES

Wonho Bae, Mohamed Osama Ahmed, Frederick Tung, Gabriel L. Oliveira

Abstract:

A temporal point process (TPP) is a stochastic process where its realization is a
sequence of discrete events in time. Recent work in TPPs model the process using
a neural network in a supervised learning framework, where a training set is a
collection of all the sequences. In this work, we propose to train TPPs in a meta
learning framework, where each sequence is treated as a different task, via a novel
framing of TPPs as neural processes (NPs). We introduce context sets to model
TPPs as an instantiation of NPs. Motivated by attentive NP, we also introduce
local history matching to help learn more informative features. We demonstrate
the potential of the proposed method on popular public benchmark datasets and
tasks, and compare with state-of-the-art TPP methods