Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference

• URL: http://arxiv.org/abs/2309.02868v2
• Date: Sat, 7 Oct 2023 02:14:44 GMT
• Title: Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference
• Authors: Yan Wang, Zhixuan Chu, Tao Zhou, Caigao Jiang, Hongyan Hao, Minjie Zhu, Xindong Cai, Qing Cui, Longfei Li, James Y Zhang, Siqiao Xue, Jun Zhou
• Abstract summary: Temporal point processes(TPPs) are the standard method for modeling such. Existing TPP models have focused on the conditional distribution of future events instead of explicitly modeling event interactions, imposing challenges for event predictions. We propose a novel approach that leverages a Neural Inference (NRI) to learn a graph that infers interactions while simultaneously learning dynamics patterns from observational data.
• Score: 21.51753838306655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Asynchronous time series, also known as temporal event sequences, are the basis of many applications throughout different industries. Temporal point processes(TPPs) are the standard method for modeling such data. Existing TPP models have focused on parameterizing the conditional distribution of future events instead of explicitly modeling event interactions, imposing challenges for event predictions. In this paper, we propose a novel approach that leverages Neural Relational Inference (NRI) to learn a relation graph that infers interactions while simultaneously learning the dynamics patterns from observational data. Our approach, the Contrastive Relational Inference-based Hawkes Process (CRIHP), reasons about event interactions under a variational inference framework. It utilizes intensity-based learning to search for prototype paths to contrast relationship constraints. Extensive experiments on three real-world datasets demonstrate the effectiveness of our model in capturing event interactions for event sequence modeling tasks. Code will be integrated into the EasyTPP framework.

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