Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference
This work addresses the challenge of event prediction in temporal point processes for applications across various industries, representing an incremental improvement by integrating relational inference.
The paper tackled the problem of modeling asynchronous time series by proposing a novel approach that learns event interactions and dynamics patterns, demonstrating effectiveness on three real-world datasets.
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.