使用概率深度学习重建和预测随机动态系统
1School of Computer and Information Technology (School of Big Data), Shanxi University, Taiyuan 030006, China.
Chaos (Woodbury, N.Y.)
|May 1, 2025
概括
本研究引入了用于时间序列预测的深度学习模型,该模型有效地捕捉了系统的不确定性. 新的深度随机时间延迟嵌入模型提高了预测准确性和稳定性,即使在有噪音的数据.
科学领域:
- 动态系统和时间序列分析.
- 机器学习和人工智能的人工智能
- 不确定性定量化 不确定性定量化
背景情况:
- 动态系统中的随机效应为数据驱动的重建和预测带来了显著的复杂性.
- 现有的方法往往难以充分解决不确定性,限制预测准确性和稳定性.
- 准确的随机性建模对于理解和预测复杂系统至关重要.
研究的目的:
- 开发一个包含不确定性学习的深度学习模型,以改进时间序列预测.
- 提出一种新的深度随机时间延迟嵌入模型,能够捕捉和利用系统的不确定性.
- 在随机效应的存在下,提高预测的稳定性和准确性.
主要方法:
- 构建一个深度概率捕捉器,以捕获在重建映射中的不确定性信息.
- 将不确定性表示作为元信息集成到时间延迟嵌入中.
- 开发一个深度随机时间延迟嵌入模型,用于多步时间序列预测.
主要成果:
- 拟议的模型在洛伦兹系统和现实世界数据集上都显示出与现有方法相比更高的性能.
- 该模型在噪音条件下表现出强大的预测能力.
- 实现了有效捕获系统随机性和提高预测准确度.
结论:
- 深度随机时间延迟嵌入模型提供了一种强大的方法来处理时间序列预测中的不确定性.
- 纳入不确定性学习显著提高动态系统预测的准确性和稳定性.
- 这种方法为数据驱动的复杂随机系统的重建和预测提供了有价值的工具.
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