English(EN) Generative Modeling of Approximately Periodic Time Series by a Posterior-Weighted Gaussian Process

新的高斯过程模型生成近似周期性时间序列

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-13 08:17

研究人员开发了一种新的生成模型，用于处理表现出近似周期性行为的时间序列数据。该模型利用具有新型核函数的高斯过程（GP），能够有效地捕捉重复数据之间的共同结构以及它们之间的细微变化。该方法将重复内部的动态与重复间的变异性分离开来，从而能够生成逼真的合成轨迹。 AI

影响引入了一种对数据中复杂、重复模式进行建模的新颖方法，有可能增强工业和网络物理系统的生成能力。

排序理由该集群包含一篇详细介绍时间序列数据新统计建模技术的学术论文。

AI 生成摘要 · Google Gemini · 来自 2 个来源。我们如何撰写摘要 →

报道来源 [2]

arXiv stat.ML TIER_1 English(EN) · Elias Reich, Saverio Messineo, Stefan Huber · 2026-05-14 04:00

Generative Modeling of Approximately Periodic Time Series by a Posterior-Weighted Gaussian Process

arXiv:2605.13150v1 Announce Type: new Abstract: Discrete automated processes in industrial and cyber-physical systems often exhibit a repetitive structure in which successive repetitions follow a common trajectory while differing in duration, amplitude, and fine-scale dynamics. S…
arXiv stat.ML TIER_1 English(EN) · Stefan Huber · 2026-05-13 08:17

Generative Modeling of Approximately Periodic Time Series by a Posterior-Weighted Gaussian Process

Discrete automated processes in industrial and cyber-physical systems often exhibit a repetitive structure in which successive repetitions follow a common trajectory while differing in duration, amplitude, and fine-scale dynamics. Such \emph{approximately periodic} behavior poses…