相关实验视频
使用功能时间序列分解和先进的预测模型进行高分辨率温度预测
Huda M Alshanbari1, Musaad S Aldhabani2, Naveed Iqbal3
1Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia.
Scientific reports
|February 25, 2026
概括
准确的空气温度 (AT) 预测至关重要. 使用功能时间序列分析的功能自回归模型 (FAR(1) 显示出比传统和机器学习方法更高的预测准确性和稳定性.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 统计建模 统计建模
背景情况:
- 空气温度 (AT) 显著影响环境过程,人类健康,农业和能源系统.
- 准确的AT预测对于各个部门的知情决策至关重要.
- 高频温度数据表现出顺,周期性动态,受季节周期和随机因素的影响.
研究的目的:
- 用功能时间序列 (FTS) 模型探索空气温度的预测.
- 将高频温度数据建模为平滑的每日曲线,捕捉季节周期和随机动态.
- 评估功能自回归模型 (FAR(1) 与经典统计和机器学习模型的性能.
主要方法:
- 利用光滑线和基于富里埃的函数表示来建模温度数据.
- 采用1级的功能性自回归模型 (FAR) 进行短期AT变化预测.
- 与ARIMA,VAR,人工神经网络 (ANN) 和自回归神经网络 (ARNN) 的FAR1性能进行比较,使用MAE,MAPE和RMSE指标.
主要成果:
- 与所有基准模型相比,FAR(1) 模型始终实现了较低的预测误差 (MAE,MAPE,RMSE).
- 在每月和每小时的预测时间范围内,FAR(1) 显示出更强的预测稳定性.
- 功能数据分析在利用温度数据固有的平滑和周期结构方面被证明是有效的.
结论:
- 该FAR(1) 模型为预测高频空气温度提供了一个实用和整体的方法.
- 功能时间序列分析是环境数据预测的宝贵工具,其性能优于传统方法.
- 这些发现支持该模型的应用,以改善农业,能源管理和气候变化期间的安全决策.
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