Multioutput Gaussian Process Modulated Poisson Processes for Event Prediction
Multioutput Gaussian Process Modulated Poisson Processes for Event Prediction
复制标题
多输出高斯过程调制泊松过程的事件预测
DOI：
10.1109/tr.2021.3088094
复制
发表时间：
2020-11
影响因子：
5.9
通讯作者：
Salman Jahani;Shiyu Zhou;D. Veeramani;Jeff Schmidt
中科院分区：
文献类型：
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作者：
Salman Jahani;Shiyu Zhou;D. Veeramani;Jeff Schmidt
部件更换和故障事件等事件的预测在可靠性工程中起着至关重要的作用。事件流数据通常在制造和远程服务系统中观察到。根据这样的事件流为单个单元设计预测模型是一个具有挑战性的问题，也是一个未被探索的问题。在这项工作中，我们提出了一个基于非齐次泊松过程的个体化事件预测的非参数预测框架，该过程具有强度函数上的多元高斯卷积过程(MGCP)先验。关于非均匀泊松过程强度函数的MGCP先验将相似历史单位的数据映射到当前研究单位，这便于信息共享并允许分析灵活的事件模式。为了便于推理，我们推导了一个变分推理方案，用于学习和估计所得到的MGCP调制的泊松过程模型中的参数。实验结果同时显示在合成数据和真实世界数据上，用于基于舰队的事件预测。
Prediction of events such as part replacement and failure events plays a critical role in reliability engineering. Event stream data are commonly observed in manufacturing and teleservice systems. Designing predictive models for individual units based on such event streams is challenging and an underexplored problem. In this work, we propose a nonparametric prognostic framework for individualized event prediction based on the inhomogeneous Poisson processes with a multivariate Gaussian convolution process (MGCP) prior on the intensity functions. The MGCP prior on the intensity functions of the inhomogeneous Poisson processes maps data from similar historical units to the current unit under study which facilitates sharing of information and allows for analysis of flexible event patterns. To facilitate inference, we derive a variational inference scheme for learning and estimation of parameters in the resulting MGCP modulated Poisson process model. Experimental results are shown on both synthetic data as well as real-world data for fleet-based event prediction.