基于SARIMA-EG-ECM模型的相对湿度预测与共变量和错误校正
Jiajun Guo1, Liang Zhang1, Ruqiang Guo1
1College of Science, Northwest A and F University, Yangling, Shaanxi 712100 China.
概括
预测相对湿度 (RH) 对各种行业至关重要. 一个新的混合模型,SARIMA-EG-ECM (SEE),通过分析气象变量与长期和短期关系,有效地预测RH.
科学领域:
- 气象学和气候学
- 环境科学 环境科学
背景情况:
- 相对湿度 (RH) 是一个重要的大气变量,对天气预报,气候研究,农业和公共卫生有重大影响.
- 准确的RH预测对于各种关键部门的知情决策至关重要.
研究的目的:
- 调查共变量和错误校正对相对湿度 (RH) 预测的影响.
- 提出和评估一种新的混合模型,以提高RH预测.
主要方法:
- 开发了一种混合的季节性自回归集成移动平均线 (SARIMA) 与恩格尔-格兰杰 (EG) 协同集成和错误纠正模型 (ECM),称为SARIMA-EG-ECM (SEE).
- 将SEE模型应用于中国海农业生态实验站的气象数据.
- 使用气象变量如空气温度 (TEMP),露点温度 (DEWP),降水量 (PRCP),大气压 (ATMO),海平面压力 (SLP) 和土壤温度 (40ST) 作为共变量.
主要成果:
- 在RH和TEMP,DEWP,PRCP,ATMO,SLP和40ST之间建立了长期平衡关系 (共同集成).
- 通过ECM识别了DEWP,ATMO和SLP波动对RH波动的显著短期影响.
- 在SEE模型中,随着预测时间的延长 (6-12个月),性能略有下降,但表现优于SARIMA和长短期记忆 (LSTM) 模型.
结论:
- 该SEE模型通过整合长期平衡和短期动态关系,为RH预测提供了一个强大的框架.
- 这些发现强调了考虑多种气象共变量和错误纠正机制对于准确的RH预测的重要性.
- 拟议的混合方法为气象应用中的RH预测提供了传统时间序列和深度学习模型的优越替代方案.
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