动力系统递推分析的趋势

Norbert Marwan, K. Hauke Kraemer
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引用次数: 0

摘要

近十年来,基于递推图的数据分析取得了一系列重要而令人振奋的发展,并进一步挖掘了其应用潜力。我们将简要介绍一些重要的创新发展,如计算方法的改进、替代性递归定义(类事件、多尺度、异质和时空递归)和参数选择的思路、递归量化措施的理论考虑、新的递归量化指标(如用于过渡检测和因果关系检测)以及校正方案。最近,通过将递归图与机器学习相结合,我们看到了新的前景。最后,我们提出了一些开放性问题,并展望了未来的方法论研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Trends in recurrence analysis of dynamical systems
The last decade has witnessed a number of important and exciting developments that had been achieved for improving recurrence plot based data analysis and to widen its application potential. We will give a brief overview about important and innovative developments, such as computational improvements, alternative recurrence definitions (event-like, multiscale, heterogeneous, and spatio-temporal recurrences) and ideas for parameter selection, theoretical considerations of recurrence quantification measures, new recurrence quantifiers (e.g., for transition detection and causality detection), and correction schemes. New perspectives have recently been opened by combining recurrence plots with machine learning. We finally show open questions and perspectives for futures directions of methodical research.
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