多种归算方法:每日黄金价格的案例研究
Ala Alrawajfi1,2, Mohd Tahir Ismail1, Sadam Al Wadi3
1School of Mathematical Science, Universiti Sains Malaysia, Penang, Penang, Malaysia.
PeerJ. Computer science
|December 16, 2024
概括
这项研究评估了缺少金融时间序列数据的数据归算方法,发现k-最近邻居 (KNN) 归算对黄金价格最准确. 性能下降,缺失数据比例增加.
科学领域:
- 金融计量经济学 金融计量经济学
- 数据科学是数据科学.
- 统计建模 统计建模
背景情况:
- 缺失的值在财务时间序列数据收集中是一个常见的挑战.
- 准确的数据归算对于可靠的财务分析和预测至关重要.
研究的目的:
- 为了比较各种数据归算技术对金融时间序列数据的有效性.
- 确定每日黄金价格数据的最稳健的归算方法.
- 评估缺失数据比例对归算准确性的影响.
主要方法:
- 平均归算,k-最近邻居 (KNN),热甲板,随机森林,支向量机 (SVM) 和spline归算的评估.
- 使用实际的每日黄金收盘价格进行验证.
- 使用指标进行绩效评估:平均误差 (ME),平均绝对误差 (MAE),根平均平方误差 (RMSE),平均百分比误差 (MPE) 和平均绝对百分比误差 (MAPE).
主要成果:
- K-最近邻居 (KNN) 归算在所有评估的准确度指标中显示出卓越的性能.
- 所有测试的归算方法的预测准确性随着缺失数据的百分比增加而下降.
- 分线归算和随机森林显示中等性能,而平均归算和热甲板效果较差.
结论:
- 建议使用K-nearest neighbor (KNN) 作为一种高效且可靠的方法,用于在财务时间序列数据中赋值缺失值.
- 缺失数据的比例显著影响归算技术的精度.
- 进一步的研究可以探索复杂的金融数据集的混合归算方法或先进的机器学习模型.
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