混合动力学的建模和推断以及因果突发特征的检测
William Casey1, Leigh Metcalf2, Shirshendu Chatterjee3
1United States Naval Academy, Annapolis, MD, US. wcasey@usna.edu.
Scientific reports
|January 14, 2026
概括
本研究引入了一种新的自适应物流模型 (ALM),用于处理复杂的,不断变化的现实世界的动态,在时间序列预测和事件检测方面表现优于经典模型.
科学领域:
- 数学建模的数学建模
- 流行病学 流行病学
- 时间序列分析时间序列分析
背景情况:
- 现实世界的过程往往涉及易受变化的非线性动态.
- 经典模型与新出现的过程和参数转移作斗争,如COVID-19数据所示.
- 像SIR这样的现有流行病学模型受到多波浪事件的挑战.
研究的目的:
- 开发一种新的数学框架来建模具有变化点的动态过程.
- 创建一个适应模型,以多个因果生成过程为准.
- 为了提高复杂时间序列数据的预测准确性和可解释性.
主要方法:
- 一个新的框架,将数据视为因果过程与参数变化点的混合物.
- 开发适应物流模型 (ALM) 作为物流模型的混合物.
- 应用非线性最小平方优化和规范化用于参数估计.
主要成果:
- 在预测COVID-19病例数量方面,ALM表现出具有竞争力的准确性.
- 该模型有效地检测动态变化点并识别因果事件.
- 与传统方法相比,ALM保留了更少,更易于解释的参数.
结论:
- 适应物流模型 (ALM) 为复杂,动态时间序列的建模和预测提供了一个强大的方法.
- ALM能够检测变化点并将其与因果事件联系起来,从而增强对现实世界现象的理解.
- 该框架在各种领域具有广泛的适用性,包括水文,经济和网络安全.
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