一个基于Koopman操作者的预测算法及其应用于COVID-19流行病和流感病例
Igor Mezić1,2, Zlatko Drmač3, Nelida Črnjarić4
1University of California, Santa Barbara, CA, 93106, USA.
Scientific reports
|March 9, 2024
概括
本研究介绍了一种新的数据驱动预测方法,用于复杂的非线性动态系统. 它准确预测系统变化和意想不到的事件,优于传统方法.
科学领域:
- 动态系统理论 动态系统理论
- 数据驱动建模数据驱动建模
- 预测分析是一种预测分析.
背景情况:
- 经典预测模型与非线性动力学,混乱系统和突然的过程变化作斗争.
- 现有的方法通常依赖于长时间的观测序列和静止性假设.
- 预测"黑天"事件仍然是时间序列分析的一个重大挑战.
研究的目的:
- 为非线性动态系统开发一个强大的预测算法,能够处理突然变化.
- 引入一种在全球和本地预测策略之间动态切换的方法.
- 创建一个修复机制,在系统动态反转时调整预测.
主要方法:
- 该方法基于库普曼运算子理论.
- 采用一种无模型,纯粹基于数据的方法.
- 算法将全球和本地预测与动态切换和修复机制集成在一起.
主要成果:
- 开发的算法成功地预测了变化动态的系统的未来状态.
- 该方法证明了适应不可预见的事件和混乱行为的能力.
- 在COVID-19和流感病例预测上得到验证,显示了广泛的适用性.
结论:
- 这种数据驱动,无模型的方法在预测复杂的动态系统方面取得了重大进展.
- 这种方法是多功能性的,超越流行病学,扩展到各种科学和工程领域.
- 基于库普曼运算子理论的方法为自适应预测提供了一个强大的工具.
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