通过对比关系推理增强异步时间序列预测
Enhancing Asynchronous Time Series Forecasting with Contrastive Relational Inference
AI总结：
针对现有TPP模型未显式建模事件交互的问题，提出基于对比关系推理的霍克斯过程CRIHP，结合NRI与变分推理，通过强度学习对比关系约束，在三个真实数据集上验证了捕捉事件交互的有效性，代码将集成入EasyTPP。
摘要：
异步时间序列，也称为时间事件序列，是不同行业中诸多应用的基础。时间点过程（Temporal Point Processes, TPPs）是建模此类数据的标准方法。现有TPP模型专注于对未来事件的条件分布进行参数化，而非显式建模事件交互，这给事件预测带来了挑战。本文提出一种新方法，利用神经关系推理（Neural Relational Inference, NRI）学习一个关系图，在推断交互的同时从观测数据中学习动力学模式。我们的方法——基于对比关系推理的霍克斯过程（Contrastive Relational Inference-based Hawkes Process, CRIHP）——在变分推理框架下推理事件交互，利用基于强度的学习寻找原型路径以对比关系约束。在三个真实世界数据集上的大量实验证明，我们的模型在事件序列建模任务中捕捉事件交互的有效性。代码将被集成到EasyTPP框架中。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
