• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International symposium on intelligent data analysis >A Hierarchical Ornstein-Uhlenbeck Model for Stochastic Time Series Analysis
【24h】

A Hierarchical Ornstein-Uhlenbeck Model for Stochastic Time Series Analysis

机译：随机时间序列分析的分级Ornstein-Uhlenbeck模型

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Longitudinal data is ubiquitous in research, and often complemented by broad collections of static background information. There is, however, a shortage of general-purpose statistical tools for studying the temporal dynamics of complex and stochastic dynamical systems especially when data is scarce, and the underlying mechanisms that generate the observation are poorly understood. Contemporary microbiome research provides a topical example, where vast cross-sectional and longitudinal collections of taxonomic profiling data from the human body and other environments are now being collected in various research laboratories world-wide. Many classical algorithms rely on long and densely sampled time series, whereas human microbiome studies typically have more limited sample sizes, short time spans, sparse sampling intervals, lack of replicates and high levels of unaccounted technical and biological variation. We demonstrate how non-parametric models can help to quantify key properties of a dynamical system when the actual data-generating mechanisms are largely unknown. Such properties include the locations of stable states, resilience of the system, and the levels of stochastic fluctuations. Moreover, we show how limited data availability can be compensated by pooling statistical evidence across multiple individuals or studies, and by incorporating prior information in the models. In particular, we derive and implement a hierarchical Bayesian variant of Ornstein-Uhlenbeck driven t-processes. This can be used to characterize universal dynamics in univariate, unimodal, and mean reversible systems based on multiple short time series. We validate the model with simulated data and investigate its applicability in characterizing temporal dynamics of human gut microbiome.
机译：纵向数据在研究中是无处不在的，并且经常被大量静态背景信息所补充。但是，缺乏用于研究复杂和随机动力系统的时间动态的通用统计工具，尤其是在数据稀缺的情况下，并且生成该观测值的基本机制了解得很少。当代的微生物组研究提供了一个典型的例子，目前，全世界各个研究实验室正在收集来自人体和其他环境的分类学概况数据的大量横截面和纵向集合。许多经典算法依赖于长时间且密集采样的时间序列，而人类微生物组研究通常具有更有限的样本量，较短的时间跨度，稀疏的采样间隔，缺乏重复性以及高水平的无法解释的技术和生物学差异。当实际的数据生成机制很大程度上未知时，我们将演示非参数模型如何帮助量化动态系统的关键属性。这些属性包括稳定状态的位置，系统的弹性以及随机波动的水平。此外，我们展示了如何通过汇总多个个体或研究中的统计证据以及将先前的信息纳入模型来补偿有限的数据可用性。特别是，我们推导并实现了由Ornstein-Uhlenbeck驱动的t过程的贝叶斯分层结构。这可用于表征基于多个短时间序列的单变量，单峰和均值可逆系统中的通用动力学。我们用模拟数据验证该模型，并研究其在表征人类肠道微生物组时间动态方面的适用性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号