使用加权集体学习预测建筑公司的股价
1Department of Statistics and Data Science, National University of Singapore, Lower Kent Ridge Road, 119077, Singapore.
Heliyon
|June 13, 2024
概括
本研究介绍了一种混合人工智能 (AI) 模型,将人工神经网络 (ANN),高斯过程回归 (GPR) 和分类和回归树 (CART) 结合起来,用于准确的建筑股价预测. 该模型实现了96.6%的准确性,超过了以前的方法.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 金融建模金融建模
背景情况:
- 股票价格预测是复杂的,特别是在建筑公司,往往需要多个模型.
- 现有的单一模型方法与股票市场数据的固有复杂性和条件依赖性作斗争.
研究的目的:
- 开发一种混合组合模型,以提高建筑行业的股价指数预测准确度.
- 为了提高预测性能,利用各种机器学习算法的互补优势.
主要方法:
- 开发了一个整体模型,集成了人工神经网络 (ANN),高斯过程回归 (GPR) 和分类和回归树 (CART).
- 使用Cuckoo Search (CS) 算法来优化集合中的每个单个模型的权重系数.
- 一种加权平均技术结合了基于优化系数的预测.
主要成果:
- 拟议的混合模型实现了建筑股票价格指数的平均预测准确率为96.6%.
- 库库搜索优化显著减少了预测错误,与以前的方法相比至少有2.4%的改进.
- 对现实世界建筑公司股票数据的实证评估证明了该模型的卓越准确性.
结论:
- 混合组合模型有效地提高了建筑公司的股票价格指数预测准确度.
- 整合Cuckoo搜索优化可以提高预测模型的可靠性和性能.
- 拟议的系统为现实世界股票市场分析和预测提供了有价值的工具.
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