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arXiv 2409.04110nlin.CDphysics.comp-phphysics.data-an
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动力系统递归分析的趋势

Trends in recurrence analysis of dynamical systems

Norbert Marwan, K. Hauke Kraemer

AI总结:

本文综述基于递归图的动力系统数据分析近十年进展,涵盖计算、递归定义、参数选择、量化指标、校正方案及机器学习结合,并指出未来研究问题。

摘要:

过去十年间,在改进基于递归图的数据分析并拓展其应用潜力方面,已取得许多重要且令人振奋的进展。我们将简要概述重要且具创新性的发展,包括计算改进、替代性递归定义(类事件、多尺度、异质和时空递归)以及参数选择思路、递归量化测度的理论思考、新的递归量化指标(例如用于转变检测和因果关系检测)以及校正方案。近来,将递归图与机器学习相结合开辟了新的前景。我们最后展示方法学研究未来方向中的开放问题与展望。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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