一个可解释的模型来预测股票价格走势,基于层次信念规则的基础
Xiuxian Yin1, Xin Zhang2, Hongyu Li1
1Harbin Normal University, Harbin, 150025, China.
Heliyon
|June 1, 2023
概括
本研究介绍了一种层次信念规则基础 (HBRB-I) 模型,用于可解释的股票价格运动预测. 该模型提高了库存预测的透明度,同时保持了预测准确度.
科学领域:
- 金融预测 财务预测
- 人工智能的人工智能
- 专家系统 专家系统
背景情况:
- 预测股票价格变动对于市场稳定至关重要.
- 现有的信念规则基础 (BRB) 模型提供了可解释性,但在股票预测过程中可能会失去它.
- 为了可靠的股票市场决策,需要提高可解释性.
研究的目的:
- 提出一个可解释的股票价格运动预测模型,使用层次的信念规则基础 (HBRB-I).
- 在整个股票预测建模过程中确保可解释性.
- 提高库存预测方法的透明度.
主要方法:
- 为信念规则基础 (BRB) 构建一个层次结构,以确保初始建模的可解释性.
- 使用证据推理 (ER) 方法作为预测过程的透明推理引擎.
- 设计一个新的投影共变矩阵适应进化策略 (P-CMA-ES) 算法,并使用可解释性标准进行优化.
主要成果:
- 获得了1.69E-04.4的最终平均平方误差.
- 保持了与最初的BRB模型可比的预测准确性.
- 显著提高了股票价格运动预测模型的解释性.
结论:
- 拟议的等级信念规则基础与可解释性 (HBRB-I) 模型有效平衡短期股票预测中的准确性和可解释性.
- 层次结构,证据推理和专门的P-CMA-ES算法的集成确保了建模,推理和优化的透明度.
- 未来的工作将涉及收集更多数据以更新规则并进一步增强预测能力.
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