新型灰狼优化器基于GARCH和ARIMA模型的参数选择,用于股票价格预测
Sneha S Bagalkot1,2, Dinesha H A1,3, Nagaraj Naik4
1Nagarjuna College of Engineering and Technology, Bengaluru and Visvesvaraya Technological University, Belagavi, India.
PeerJ. Computer science
|January 10, 2024
概括
本研究介绍了一种灰狼优化器 (GWO) 方法,用于优化自行回归集成移动平均 (ARIMA) 和通用自行回归条件异构度 (GARCH) 模型中的参数,从而提高股票价格预测的准确性.
科学领域:
- * 计算金融学
- * 金融领域的人工智能
- * 计量经济学 计量经济学
背景情况:
- * 股价数据表现出复杂的非线性模式和动态.
- *传统的自回归集成移动平均线 (ARIMA) 和通用自回归条件异构度 (GARCH) 模型中的参数选择由于波动性而具有挑战性.
- *手动参数选择方法耗时,依赖于试错.
研究的目的:
- * 提出一种基于灰狼优化器 (GWO) 的新方法,用于ARIMA和GARCH模型中的最佳参数选择.
- *通过优化模型参数来提高股票价格预测的准确性.
- * 解决金融时间序列建模中手动参数选择的局限性.
主要方法:
- * 实现了灰狼优化器 (GWO) 算法,灵感来自狼群的掠夺行为.
- * GWO的应用用于在ARIMA和GARCH模型中代选择最佳参数.
- *使用标准财务时间序列指标对拟议模型的评估:根平均平方误差 (RMSE),平均平方误差 (MSE) 和平均绝对误差 (MAE).
主要成果:
- * 基于GWO的参数选择方法与传统方法相比显示出更好的性能.
- * 拟议的模型比现有的传统ARIMA和GARCH模型实现了5%至8%的性能提升.
- *优化的参数导致更准确的股票价格预测,如低误差指标所证明的那样.
结论:
- *灰狼优化器 (GWO) 是一种有效的元启发方法,用于优化用于股票价格预测的ARIMA和GARCH模型中的参数.
- * 这种方法比手动,试错方法提供了显著的改进,减少了时间和提高了准确性.
- * 该研究强调了人工智能驱动的优化技术在财务预测中的潜力.
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