多变量朗格温方程的数据驱动重建,以建模复杂系统
Antonio Malpica-Morales1, Miguel A Durán-Olivencia1,2, Serafim Kalliadasis1
1Imperial College, Department of Chemical Engineering, London SW7 2AZ, United Kingdom.
Physical review. E
|September 16, 2025
概括
本研究引入了一种数据驱动的多变量朗格温方程 (LE) 来建模复杂系统. 该方法在没有事先知识的情况下准确地捕捉系统动态,在机械和金融市场中被证明是有效的.
科学领域:
- 复杂系统分析 复杂系统分析
- 统计力学 统计力学
- 量化金融 量化金融
背景情况:
- 建模具有复杂相互作用的复杂系统是具有挑战性的.
- 现有的方法往往需要对潜在机制的先验知识.
- 对系统可观测的准确描述对于理解行为至关重要.
研究的目的:
- 提出一个数据驱动的多变量朗格温方程 (LE),用于近似复杂系统的可观测值.
- 解开复杂系统的关键特征,而不需要先前的知识.
- 证明框架在各种应用中具有适应性和可靠性.
主要方法:
- 使用非参数技术重建LE中的漂移和扩散术语.
- 克莱默斯-莫亚尔系数用于LE术语识别的应用.
- 与机械系统 (可比电位中的粒子) 和财务数据 (电价,货币汇率) 的基准测试.
主要成果:
- 该框架准确地识别了平衡值,元稳定性区域和不同的扩散行为.
- 成功应用于金融市场 (前一天电价,货币汇率),在此之前没有使用LE.
- 演示了功能不可知论的方法,与特定领域的价格方程模型形成对比.
结论:
- 拟议的非参数的多变量LE框架提供了一个可靠的,数据驱动的方法来建模复杂的系统.
- 该方法有效地提取相关信息和系统特征,而不需要先验领域知识.
- 这种方法为分析包括金融市场在内的多种复杂系统提供了强大的工具.
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