股票价格预测中的多层次前景:ICE2DE-MDL
Zinnet Duygu Akşehir1, Erdal Kılıç1
1Computer Engineering, Ondokuz Mayis University Samsun, Samsun, Turkey.
PeerJ. Computer science
|July 10, 2024
概括
本研究介绍了ICE2DE-MDL模型,这是一种新的混合方法,使用和分解来消除财务数据以准确预测股票价格. 该模型在预测股票市场指数和个人股票方面表现优于现有的方法.
科学领域:
- * 计算金融学
- * 数据科学数据科学
- * 金融时间序列分析
背景情况:
- * 金融时间序列数据往往含有噪音,妨碍准确的价格预测.
- *现有的库存预测模型在降低噪音和预测准确性方面扎.
- *需要新的方法来提高股票市场预测的可靠性.
研究的目的:
- * 提出和评估一种新的混合模型,ICE2DE-MDL,用于股票收盘价格预测.
- * 通过使用和ICEEMDAN方法学,有效地消除金融时间序列中的噪音.
- * 为了比较ICE2DE-MDL与现有的混合模型在库存预测中的性能.
主要方法:
- * 开发了ICE2DE-MDL模型,集二次分解,和机器/深度学习.
- * 应用了使用和两级改进的完整合体实证模式分解与适应噪声 (ICEEMDAN) 的无声化方法.
- *使用长期短期内存 (LSTM),LSTM-BN,Gated Recurrent Unit (GRU) 和支持向量回归 (SVR) 在被拒绝的内在模式函数 (IMF) 上.
主要成果:
- * ICE2DE-MDL模型实现了高精度,R平方值在0.905到0.998.99之间.
- *绩效指标 (RMSE,MAE,MAPE) 证明了该模型在八个股票市场指数和三个股票数据集中的有效性.
- * ICE2DE-MDL在预测股票市场指数和个人股票方面显著优于现有的混合模型.
结论:
- * ICE2DE-MDL模型通过有效处理杂的财务数据,为股票价格预测提供了优越的方法.
- * 这项研究展示了和ICEEMDAN在库存数据中消除噪音的首次已知的应用.
- *这项研究为金融时间序列预测领域提供了一种新的,高性能的混合模型.
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