多重目标逃脱鸟类搜索优化及其在基于变压器模型的股票市场预测中的应用
Dedai Wei1, Zimo Wang2, Minyu Qiu3
1College of Economics, Shenyang University, Shenyang, 110000, China.
Scientific reports
|February 17, 2025
概括
一个新的多目标逃生鸟算法 (MOEBS) 优化了变压器模型的股票市场预测,大大提高了比传统方法的准确性和稳定性. 这种方法克服了超级参数调整的局部最佳挑战,以提高财务预测.
科学领域:
- 计算金融与经济学
- 人工智能和机器学习
- 优化算法 优化算法
背景情况:
- 股票市场预测对于金融回报至关重要,像变压器这样的深度学习模型显示出有希望.
- 对于这些模型,现有的超参数优化方法往往存在局限性,特别是在单一目标的方法中,它们会陷入局部最佳状态.
- 有效的超参数优化是释放金融市场先进预测模型的全部潜力的关键.
研究的目的:
- 在股票市场预测模型中引入多目标逃生鸟算法 (MOEBS) 进行增强的超参数优化.
- 提出MOEBS-Transformer架构,以提高财务时间序列预测的效率和有效性.
- 在复杂的金融建模场景中解决单一目标优化的局限性.
主要方法:
- 在基准数据集 (ZDT,DTLZ,WFG) 上验证的MOEBS与MOMVO,MSSA和MOEAD等算法相比,使用GD,间距,IGD和HV的指标.
- 应用MOEBS来优化亚马逊,谷歌和Uniqlo股票收盘价格的变压器模型超参数,考虑多目标标准 (培训/测试RMSE,错误差异).
- 将MOEBS-Transformer与CNN,LSTM,BiLSTM,GRU,传统的变压器和通过MOMVO,MSSA,MOEAD,随机搜索,网格搜索和贝叶斯优化优化的变压器模型进行比较.
主要成果:
- 与CNN,LSTM,BiLSTM和GRU相比,变压器模型被认为是股票市场预测的优势.
- 与其他优化技术相比,MOEBS优化的变压器模型显示出更高的预测准确性和稳定性 (R2,RMSE,RPD指标).
- MOEBS-变压器的性能明显超过了MOMVO-变压器,MSSA-变压器,MOEAD-变压器,RS-变压器,GS-变压器和BO-变压器.
结论:
- 拟议的多目标逃生鸟算法 (MOEBS) 有效地优化了变压器模型的股票市场预测,克服了局部最佳问题.
- MOEBS-Transformer为财务预测提供了强大而准确的解决方案,性能优于现有的先进方法.
- 这项研究为复杂的优化问题提供了一种新的方法,并推进了股票市场预测技术.
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