提高股票指数预测:一种混合LSTM-PSO模型,以提高预测准确度
Xiaohua Zeng1, Changzhou Liang1, Qian Yang1
1School of Economics and Trade, Guangzhou Xinhua University, Dongguan, China.
PloS one
|January 14, 2025
概括
本研究介绍了粒子群集优化-长期短期记忆 (PSO-LSTM) 模型,用于增强股票价格预测. 通过优化LSTM参数,PSO-LSTM显著提高了准确性,优于其他机器学习方法.
科学领域:
- 计算金融是指计算金融.
- 人工智能的人工智能
- 时间序列分析时间序列分析
背景情况:
- 由于市场波动和历史数据的长期依赖,股票价格预测是复杂的.
- 长短期记忆 (LSTM) 神经网络参数的手动调整极大地影响了预测准确性.
- 现有的方法经常在优化LSTM超参数以获得最佳性能方面扎.
研究的目的:
- 提出和评估一种用于股票价格预测的新型PSO-LSTM模型.
- 为了利用粒子群集优化 (PSO) 实现高效的LSTM参数调整.
- 为了证明该模型在各种全球股票指数中的有效性.
主要方法:
- 实现混合PSO-LSTM模型,将PSO的优化能力与LSTM的时间序列分析优势相结合.
- 使用PSO算法对LSTM参数进行系统调整.
- 在六个全球股票指数的实验验证,与其他七个机器学习算法的性能进行比较.
主要成果:
- 该PSO-LSTM模型在真实世界股票市场数据上表现出卓越的性能和高预测准确性.
- 增加的PSO代与减少的模型损失相关,表明强大的趋同.
- 在不同的追溯数据期中观察到一致的准确性.
结论:
- 该PSO-LSTM模型在股票价格预测准确性和效率方面取得了显著的进步.
- PSO有效地优化了LSTM参数,克服了手动调的局限性.
- 该模型在各种股票指数和时间跨度上的强表现凸显了其实际适用性.
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