Meta Temporal Point Processes

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

Keywords: Temporal Point Process, Asynchronous Time Series, Meta-learning

TL;DR: We present a novel approach to train temporal point processes in a meta learning framework.

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.

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