一个新的决策组合框架:专注定制的BiLSTM和XGBoost用于投机股票价格预测
Riaz Ud Din1,2, Salman Ahmed2,3, Saddam Hussain Khan1
1Artificial Intelligence Lab, Department of Computer Systems Engineering, University of Engineering and Applied Sciences (UEAS), Swat, Pakistan.
PloS one
|April 16, 2025
概括
一个新的框架,ACB-XDE,通过将双向长期短期内存 (BiLSTM) 与注意力机制和XGBoost.com相结合,准确地预测比特币价格. 这种创新方法提高了在波动性金融市场的投资风险管理.
科学领域:
- * 计算金融和算法交易
- * 机器学习用于金融时间序列分析.
- * 加密货币市场预测和风险管理
背景情况:
- *准确预测投机性股票价格对于投资风险管理至关重要.
- * 金融市场表现出波动性和复杂的顺序依赖性,对传统的预测模型构成挑战.
- *现有的方法往往难以捕捉比特币-美元 (BTC-USD) 等加密货币市场的动态性质.
研究的目的:
- * 提出一个新的框架,注意力定制的BiLSTM-XGB决策集 (ACB-XDE),用于预测每日BTC-USD收盘价格.
- *通过结合深度学习和组合方法的混合方法来提高预测准确性和模型可解释性.
- * 在高度波动的市场中证明框架的有效性.
主要方法:
- * 集成定制的双向长期短期记忆 (BiLSTM) 与一种新的注意力机制,以捕捉顺序依赖和市场趋势.
- * 整合了XGBoost算法来处理非线性关系并提高模型的稳定性.
- * 应用一个错误的反向方法来进行代重量调整和集合回归,以提高概括性.
主要成果:
- *ACB-XDE框架在BTC-USD数据上取得了卓越的表现 (10/01/2014至01/08/2023).
- *实现了0.37%的平均绝对百分比误差 (MAPE),平均绝对误差 (MAE) 为84.40,根平均平方误差 (RMSE) 为106.14.
- * 与最先进的模型相比,显著改善,包括与注意力-BiLSTM相比,MAPE减少了27.45%.
结论:
- * ACB-XDE框架提供了一种有效的技术,用于在动态的金融市场中进行知情决策.
- * 拟议的模型在处理BTC-USD价格预测的复杂性方面表现出高准确性和稳定性.
- *这种方法为投机性加密货币交易中的投资风险管理提供了有价值的工具.
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