论文标题
用CRQA进行复发定量分析的一维和多维方法
Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa
论文作者
论文摘要
复发定量分析是一种广泛使用的方法，用于表征时间序列中的模式。本文介绍了一项全面的调查，用于进行广泛的基于复发的分析，以量化单个和多元时间序列的动态结构，并捕获领导者追随者关系的耦合属性。复发量化分析（RQA）及其所有变体的基础知识正式逐步引入，从最简单的自动转变到最先进的多元案例。重要的是，我们展示了如何使用我们的CRQA 2.0软件包的基本更新版本在R中的单个计算框架下部署此类RQA方法。该软件包包括基于复发分析的最新进展的实现，其中包括用于多元数据的应用，以及改进的分类数据的熵计算。我们显示了包装的具体应用程序以示例数据，以及有关其功能的详细说明以及其使用情况的一些准则。
Recurrence quantification analysis is a widely used method for characterizing patterns in time series. This article presents a comprehensive survey for conducting a wide range of recurrence-based analyses to quantify the dynamical structure of single and multivariate time series, and to capture coupling properties underlying leader-follower relationships. The basics of recurrence quantification analysis (RQA) and all its variants are formally introduced step-by-step from the simplest auto-recurrence to the most advanced multivariate case. Importantly, we show how such RQA methods can be deployed under a single computational framework in R using a substantially renewed version our crqa 2.0 package. This package includes implementations of several recent advances in recurrence-based analysis, among them applications to multivariate data, and improved entropy calculations for categorical data. We show concrete applications of our package to example data, together with a detailed description of its functions and some guidelines on their usage.
