A Hybrid Air Quality Prediction Method Based on VAR and Random Forest  

作　　者：Minghao Yi Fuming Lin Minghao Yi;Fuming Lin(College of Mathematical and Statistics, Sichuan University of Science and Engineering, Zigong, China)

机构地区：[1]College of Mathematical and Statistics, Sichuan University of Science and Engineering, Zigong, China

出　　处：《Journal of Computer and Communications》2025年第2期142-154,共13页电脑和通信(英文)

摘　　要：To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models. In the theoretical section, the model introduction and estimation algorithms are provided. In the empirical analysis section, global air quality data from 2022 to 2024 are used, and the proposed method is applied. Specifically, principal component analysis (PCA) is first conducted, and then VAR and Random Forest methods are used for prediction on the reduced-dimensional data. The results show that the RMSE of the hybrid model is 45.27, significantly lower than the 49.11 of the VAR model alone, verifying its superiority. The stability and predictive performance of the model are effectively enhanced.To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models. In the theoretical section, the model introduction and estimation algorithms are provided. In the empirical analysis section, global air quality data from 2022 to 2024 are used, and the proposed method is applied. Specifically, principal component analysis (PCA) is first conducted, and then VAR and Random Forest methods are used for prediction on the reduced-dimensional data. The results show that the RMSE of the hybrid model is 45.27, significantly lower than the 49.11 of the VAR model alone, verifying its superiority. The stability and predictive performance of the model are effectively enhanced.

关 键 词：Var Model Principal Component Analysis Random Forest Model 

分 类 号：F83[经济管理—金融学]