调整深度学习以预测不同样本下的价格:贝叶斯优化与随机搜索对比
Alicia Estefania Antonio Figueroa1, Salim Lahmiri1
1Department of Supply Chain and Business Technology Management, John Molson School of Business, Concordia University, Montreal, QC H3H 0A1, Canada.
Entropy (Basel, Switzerland)
|February 27, 2026
概括
预测价格的最佳模型是使用贝叶斯优化 (BO) 的长短期记忆 (LSTM). 这种深度学习方法,LSTM-BO,准确地预测价格趋势在每日,每周和每月的数据.
科学领域:
- 计算金融是一种计算金融.
- 机器学习应用程序 机器学习应用程序
- 计量经济学 计量经济学 计量经济学
背景情况:
- 价格表现出复杂的,非线性动态.
- 准确的价格预测对于市场参与者来说至关重要.
- 传统模型可能会与商品市场复杂的行为作斗争.
研究的目的:
- 实施和比较用于价格预测的深度学习模型.
- 为了评估贝叶斯优化 (BO) 与随机搜索 (RS) 在模型调整中的有效性.
- 为了确定最佳的模型预测现货价格在不同的时间范围内.
主要方法:
- 使用长期短期记忆 (LSTM) 和深度前神经网络 (FFNN) 作为深度学习模型.
- 雇员支持向量回归 (SVR) 作为基线比较模型.
- 使用贝叶斯优化 (BO) 和随机搜索 (RS) 对每日,每周和每月数据进行调整的模型.
主要成果:
- 使用贝叶斯优化 (LSTM-BO) 优化的LSTM模型表现出卓越的性能.
- LSTM-BO 始终实现了最低的根平均平方误差 (RMSE) 和平均绝对误差 (MAE),以及最高的R平方 (R2).
- 使用贝叶斯优化调整的模型在所有测试的配置中,通常优于使用随机搜索调整的模型.
结论:
- 对于现货价格预测,LSTM-BO非常有效.
- 贝叶斯优化是一种比随机搜索更有效的调整方法,用于这些预测模型.
- 深度学习,特别是LSTM与BO,为捕捉复杂的金融市场行为提供了显著的优势.
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