基于ARIMA-GRU/LSTM混合模型的上海复合指数开盘价格差的建模
Yuancheng Si1, Saralees Nadarajah2, Zongxin Zhang1
1School of Economics, Fudan University, ShangHai, PR China.
PloS one
|March 13, 2024
概括
将ARIMA与LSTM和GRU网络相结合的混合模型显著改善了上海复合指数开盘价差预测,优于传统方法以更好地预测股票市场.
科学领域:
- * 金融市场和计量经济学
- * 计算金融和深度学习
背景情况:
- *准确的股票指数预测对于投资者和政策制定者来说至关重要.
- * 传统的时间序列模型与股票价格的非线性模式作斗争.
- * 上海综合指数开盘价差是市场波动和情绪的一个关键指标.
研究的目的:
- *为了提高上海复合指数开盘价格差预测的精度.
- * 开发和评估一个混合模型,将ARIMA与深度学习技术 (LSTM,GRU) 集成在一起.
主要方法:
- * 开发并比较了五种模型:ARIMA,LSTM,GRU,ARIMA-LSTM和ARIMA-GRU.
- *使用了1990年12月20日至2023年6月2日的上海综合指数的综合数据集.
- *使用平均平方误差 (MSE) 和平均绝对误差 (MAE) 评估模型性能.
主要成果:
- *混合型号 (ARIMA-LSTM和ARIMA-GRU) 显示出卓越的性能.
- *与独立模型相比,混合模型在预测开盘价格差距方面取得了更高的准确性.
- * 深度学习集成有效地捕捉了复杂的,非线性股票市场动态.
结论:
- *将ARIMA与LSTM或GRU相结合,可以提高股票市场预测的准确性.
- *混合方法为财务预测提供了更全面的工具.
- * 调查结果支持将统计和深度学习方法整合到明智的投资策略中.
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