一个游戏理论模型用于对选定的石油公司价格变化的分类
Rodica-Ioana Lung1, Florin Sebastian Duma1
1Center for the Study of Complexity, Babeș-Bolyai University, Cluj-Napoca, Romania.
PeerJ. Computer science
|June 22, 2023
概括
本研究介绍了一种新的游戏理论方法,使用纳什平衡来改善机器学习分类. 该方法准确地预测了石油价格的变化,超过了传统模型.
科学领域:
- 机器学习 机器学习
- 游戏理论 游戏理论
- 计量经济学 计量经济学 计量经济学
背景情况:
- 机器学习模型需要细微的数据理解.
- 游戏理论提供了替代解决方案概念,以增强模型的能力.
- 二元分类问题往往受益于先进的估计技术.
研究的目的:
- 从游戏理论中应用纳什平衡来估计二进制分类中的试验参数.
- 开发一个非合作游戏,数据变量作为玩家最大化日志概率贡献.
- 评估拟议方法在预测石油价格动态方面的有效性.
主要方法:
- 使用纳什平衡作为试验参数估计的解决方案概念.
- 制定一个非合作游戏,数据变量作为玩家.
- 调整一个微分进化算法来解决游戏.
- 应用该方法来分析 OMV Petrom SA 罗马尼亚与原油和布伦特油价格的价格变化.
主要成果:
- 提出的基于纳什平衡的方法在预测价格变化方面表现出卓越的表现.
- 该模型的性能优于基线探针和经典分类方法.
- 该研究提供了对影响石油公司股价的因素的见解.
结论:
- 游戏理论,特别是纳什平衡,为增强机器学习分类提供了一个强大的框架.
- 开发的方法为金融市场分析提供了强大的工具,特别是在能源部门.
- 这种方法促进了经济原理的整合到用于预测建模的机器学习中.
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