从大型流数据集中重建随机动态
1Cecil H. and Ida M. Green Institute of Geophysics and Planetary Physics, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California 92037, USA.
Physical review. E
|December 20, 2023
概括
本研究介绍了一种高效的在线方法,用于使用随机微分方程分析复杂的物理系统. 该技术能够从庞大的数据集中准确估计系统动态,克服计算限制.
科学领域:
- 物理 物理学 物理
- 应用数学 应用数学 应用数学
- 数据科学数据科学数据科学
背景情况:
- 复杂的物理系统通常使用随机微分方程 (SDEs) 建模.
- 从这些系统中分析大型时间序列数据集在计算上具有挑战性.
- 现有的方法与现代科学数据的规模扎.
研究的目的:
- 从大型时间序列数据集中开发一种计算效率高的方法来估计漂移和扩散函数.
- 克服复杂系统的传统分析技术的局限性.
- 为了使科学研究中"大数据"的分析.
主要方法:
- 使用增量,在线更新统计数据进行参数估计.
- 开发了一种新的算法,以流式的方式处理大型数据集.
- 将该方法应用于合成和实证数据集.
主要成果:
- 从大型合成数据集中成功估计了漂移和扩散函数.
- 在经验性流数据集上验证了该方法的实用性.
- 与传统方法相比,已经证明了计算效率和准确性.
结论:
- 拟议的在线方法有效地处理大规模的时间序列数据用于SDE分析.
- 这种方法促进了使用"大数据"和实时流的复杂系统的研究.
- 该方法为动态系统建模提供了一个可扩展的解决方案.
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