基于多尺度分数和深度学习的股价动态预测
1Henan Polytechnic Institute, School of Economics and Trade, Nanyang, Henan, China.
PloS one
|December 2, 2025
概括
这项研究引入了一种新的深度学习方法,使用碎形特征来预测股票价格. 该方法通过捕捉复杂的市场动态和长期相关性来提高准确性和稳定性.
科学领域:
- 量化金融 量化金融
- 计算金融是指计算金融.
- 金融时间序列分析分析
背景情况:
- 股票价格波动表现出复杂的多尺度,非线性和碎形特征.
- 现有的方法往往难以完全捕捉这些复杂的动态,以准确预测.
研究的目的:
- 开发一种新的股票价格预测方法,利用碎形特征提取和深度学习.
- 通过结合先进的分法度量和度量来增强股票价格系列的表征.
主要方法:
- 使用通用的赫斯特指数 (Hq),碎形维度 (FDq) 和多碎形光谱 (MFSq) 来进行特定尺度的表征.
- 集成的雷尼 Entropy 和通用的分数布朗运动 (GFBM) 来丰富特征表示.
- 开发了一种用于时间频域聚合的多尺度碎形特征融合机制 (MSA).
- 构建了一个多尺度的分数损失函数,包括雷尼误差,霍尔德约束和分数光谱偏差.
主要成果:
- 与现有技术相比,拟议的方法在真实市场数据上的预测性能优于现有技术.
- 实现了更高的准确性,稳定性和更好的处理极端市场波动的能力.
- 多尺度碎形方法有效地捕捉了非线性动态,并保持了股票价格固有的碎形结构.
结论:
- 这项研究为金融时间序列分析提供了一个新的理论框架.
- 介绍了碎形理论的新应用,用于改善财务预测.
- 开发的方法为理解和预测复杂的金融市场行为提供了一个强大的工具.
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