基于GA-WOA-LSTM的股票市场预测研究
1School of Science, Shenyang University of Chemical Technology, Shenyang, Liaoning, China.
PloS one
|August 27, 2025
概括
这项研究介绍了一种混合模型,它结合了遗传算法 (GA),鱼优化算法 (WOA) 和长期短期记忆 (LSTM) 来改进股票市场预测. GA-WOA-LSTM模型提高了财务时间序列的预测准确性.
科学领域:
- 金融预测和时间序列分析.
- 计算智能和机器学习应用.
- 经济建模和市场监管.
背景情况:
- 全球金融市场日益复杂,需要准确的股票市场预测投资,监管和规划.
- 传统的预测模型经常与金融时间序列数据固有的非线性依赖性和长期模式作斗争.
研究的目的:
- 提出和评估一种新的混合预测模型,即GA-WOA-LSTM,用于增强股票市场预测.
- 利用遗传算法 (GA),鱼优化算法 (WOA) 和长期短期记忆 (LSTM) 的优势,实现卓越的预测性能.
主要方法:
- 一个混合模型集成GA用于全球超参数优化,WOA用于本地搜索改进,LSTM用于时间序列建模.
- 使用LSTM神经网络,以捕捉非线性依赖和长期模式.
- 在训练和测试数据集上使用平均绝对误差 (MAE),平均绝对百分比误差 (MAPE),根平均平方误差 (RMSE) 和R2来评估模型性能.
主要成果:
- 与传统基线模型相比,GA-WOA-LSTM模型的预测准确度显著提高.
- 拟议的模型在训练和测试数据集上表现出卓越的概括能力.
- 关键性能指标 (MAE,MAPE,RMSE,R2) 表明了混合方法的有效性.
结论:
- GA-WOA-LSTM模型为财务时间序列预测提供了强大而有效的策略.
- 这项研究为现实金融市场的实际应用提供了宝贵的见解.
- 优化算法与深度学习的整合提高了股票市场预测的准确性和可靠性.
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