相关实验视频
优化适应性神经模糊推理系统模型基于混沌哈里斯霍克斯算法用于股票预测
Zahraa Elsayed Mohamed1, Ahmed Refaie Ali2, Walid Dabour3
1Department of Mathematics, Faculty of Science, Zagazig University, P.O. Box 44519, Zagazig, Egypt. zahraa_sd@yahoo.com.
Scientific reports
|September 15, 2025
概括
这项研究引入了一个优化的自适应神经模糊推理系统 (ANFIS) 与混乱哈里斯霍克斯优化 (ChHHO) 以提高股票价格预测准确度. ANFIS-ChHHO模型有效地解决了财务数据的非线性性质,优于传统方法.
科学领域:
- 计算金融是一种计算金融.
- 金融领域的人工智能
- 金融预测 财务预测
背景情况:
- 股票市场的预测是复杂的,因为有效的市场假设和波动的供需.
- 金融数据的非线性和混乱性质为准确的价格预测带来了重大挑战.
- 现有的人工智能模型经常与局部最佳和有限的搜索空间探索作斗争.
研究的目的:
- 使用优化的人工智能模型提高股价预测的准确性.
- 解决传统预测方法在捕捉金融市场动态方面的局限性.
- 开发一种结合自适应神经模糊推理系统 (ANFIS) 与混乱哈里斯霍克斯优化 (ChHHO) 的强大模型.
主要方法:
- 开发了一个优化的自适应神经模糊推理系统 (ANFIS).
- 该ANFIS模型与一个混乱的哈里斯霍克斯优化 (ChHHO) 算法集成.
- 在ANFIS模型中使用ChHHO算法来改进搜索空间探索并避免局部最佳值.
- 拟议的ANFIS-ChHHO模型是根据EGX指数股票数据进行评估的.
主要成果:
- 与传统方法相比,ANFIS-ChHHO模型表现出优异的预测性能.
- 评估指标包括根平均平方误差 (RMSE),平均绝对误差 (MAE),标准偏差 (SD) 和Theil的U.
- ChHHO算法增强了ANFIS模型在复杂的金融市场格局中导航的能力.
- 该模型在预测股市走势方面取得了更高的准确性.
结论:
- 拟议的ANFIS-ChHHO模型在人工智能驱动的股票价格预测方面取得了重大进展.
- 这种混合方法有效地处理了金融时间序列数据的非线性和混乱特征.
- 该研究强调了优化的元启发算法在提高财务预测准确度方面的潜力.
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