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Temporal information partitioning: Characterizing synergy, uniqueness, and redundancy in interacting environmental variables

机译：时间信息分区：在交互的环境变量中表征协同作用，唯一性和冗余

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摘要

Information theoretic measures can be used to identify nonlinear interactions between source and target variables through reductions in uncertainty. In information partitioning, multivariate mutual information is decomposed into synergistic, unique, and redundant components. Synergy is information shared only when sources influence a target together, uniqueness is information only provided by one source, and redundancy is overlapping shared information from multiple sources. While this partitioning has been applied to provide insights into complex dependencies, several proposed partitioning methods overestimate redundant information and omit a component of unique information because they do not account for source dependencies. Additionally, information partitioning has only been applied to time-series data in a limited context, using basic pdf estimation techniques or a Gaussian assumption. We develop a Rescaled Redundancy measure (Rs) to solve the source dependency issue, and present Gaussian, autoregressive, and chaotic test cases to demonstrate its advantages over existing techniques in the presence of noise, various source correlations, and different types of interactions. This study constitutes the first rigorous application of information partitioning to environmental time-series data, and addresses how noise, pdf estimation technique, or source dependencies can influence detected measures. We illustrate how our techniques can unravel the complex nature of forcing and feedback within an ecohydrologic system with an application to 1 min environmental signals of air temperature, relative humidity, and windspeed. The methods presented here are applicable to the study of a broad range of complex systems composed of interacting variables.
机译：信息理论方法可用于通过减少不确定性来识别源变量和目标变量之间的非线性相互作用。在信息划分中，多元互信息被分解为协同，唯一和冗余的组件。协同是仅当源一起影响目标时共享的信息，唯一性是仅由一个源提供的信息，而冗余是来自多个源的共享信息的重叠。尽管已应用此分区来提供对复杂依赖关系的了解，但一些提议的分区方法高估了冗余信息，并忽略了唯一信息的组成部分，因为它们没有考虑源依赖关系。此外，使用基本pdf估计技术或高斯假设，仅在有限的上下文中将信息分区应用于时间序列数据。我们开发了可伸缩的冗余度量（Rs）以解决源依赖性问题，并提出了高斯，自回归和混沌测试用例，以证明在存在噪声，各种源相关性和不同类型的交互作用下，它比现有技术更具优势。这项研究构成了对环境时间序列数据进行信息划分的第一个严格的应用，并讨论了噪声，pdf估计技术或源依赖关系如何影响检测到的措施。我们说明了我们的技术如何将生态水文系统中强迫和反馈的复杂性质加以揭示，并将其应用于1分钟的气温，相对湿度和风速等环境信号。本文介绍的方法适用于研究由相互作用变量组成的各种复杂系统。

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  • 来源
    《Water resources research》 |2017年第7期|5920-5942|共23页
  • 作者

    Goodwell Allison E.; Kumar Praveen;

  • 作者单位

    Univ Illinois, Dept Civil & Environm Engn, Champaign, IL 61820 USA;

    Univ Illinois, Dept Civil & Environm Engn, Champaign, IL 61820 USA|Univ Illinois, Dept Atmospher Sci, Champaign, IL 61820 USA;

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  • 正文语种 eng
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  • 入库时间 2022-08-18 03:38:41

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