一项对线性随机时间序列模型的NNAR方法的新型比较研究,用于预测网球运动员的表现
Abdullah M Almarashi1, Muhammad Daniyal2, Farrukh Jamal3
1Department of Statistics, Faculty of Science, King Abdulaziz University, 21589, Jeddah, Saudi Arabia.
BMC sports science, medicine & rehabilitation
|January 25, 2024
概括
准确的网球表现预测需要考虑比赛时间. 神经网络自动回归 (NNAR) 模型,包括时间,在预测球员排名方面优于ARIMA等传统模型.
科学领域:
- 运动分析 运动分析
- 时间序列预测时间序列预测
- 运动中的机器学习
背景情况:
- 球员表现预测对于体育决策至关重要.
- 现有的模型往往忽视了比赛安排的时间方面.
- 纳入时间对于准确的网球运动员表现预测至关重要.
研究的目的:
- 开发和评估网球运动员绩效指标的预测模型.
- 通过整合时间因素来预测球员排名.
- 帮助玩家和分析师做出明智的决策.
主要方法:
- 利用动态技术分析性能结构,使用线性和非线性时间序列模型.
- 将神经网络自动回归 (NNAR) 模型与ARIMA,ETS和TBATS进行了比较.
- 在评估中使用了根平均平方误差 (RMSE),平均绝对误差 (MAE) 和平均绝对百分比误差 (MAPE).
主要成果:
- 在所有评估指标 (RMSE,MAE,MAPE) 中,NNAR模型表现出卓越的性能.
- NNAR预测显示较窄的95%信心区间,表明准确性和可靠性提高.
- 在网球表现预测方面,NNAR模型被证明比传统模型更有效.
结论:
- 时间是预测网球运动员表现的重要因素.
- 推使用NNAR模型来预测未来的球员排名.
- 与ARIMA,ETS和TBATS等传统模型相比,这种方法提供了更准确的预测.
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