基于CED-PSO-StockNet时间序列模型的库存预测研究
Xinying Chen1, Fengjiao Yang2, Qianhan Sun3
1School of Computer and Communication Engineering, Dalian Jiaotong University, 794 Huanghe Road, Shahekou District, Dalian, Liaoning, China. chenxy1979@163.com.
Scientific reports
|November 10, 2024
概括
这项研究介绍了CED-PSO-StockNet,这是一种用于在杂环境中准确预测库存的新型时间序列模型. 该模型通过分解数据,重建组件并使用改进的粒子群优化算法优化参数来显著提高预测准确性.
科学领域:
- 量化金融 量化金融
- 机器学习 机器学习
- 时间序列分析时间序列分析
背景情况:
- 股票市场预测面临着由于高噪音环境的挑战,导致准确性低.
- 现有的模型很难有效地处理噪音,并从复杂的时间序列数据中提取相关特征.
研究的目的:
- 引入一个创新的时间序列模型,CED-PSO-StockNet,以提高库存预测的准确性.
- 在杂的金融数据环境中解决现有模型的局限性.
主要方法:
- 使用完整集体实证模式分解与自适应噪声 (CEEMDAN) 和通过极点方法估计频率的数据分解.
- 一个带有注意力机制的编码解码器框架,用于预测重建的组件.
- 使用改进的粒子优化 (IPSO) 算法进行参数优化.
主要成果:
- 根据CED-PSO-StockNet模型,库存预测准确度显著提高.
- 在浦东银行数据集上,与独立的LSTM模型相比,R2度量得到了45.59%的改进.
- 对平安银行数据集的验证证实了该模型的概括能力和显著优势.
结论:
- 该CED-PSO-StockNet模型有效地提高了在高噪音环境中库存预测的准确性.
- 基于注意力的编码解码器CEEMDAN和IPSO优化的组合为金融时间序列分析提供了强大的解决方案.
- 拟议的模型显示了在股票市场预测中实际应用的巨大潜力.
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