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has been cited by the following article:

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

  AUTHORS: Minghao Yi, Fuming Lin

  KEYWORDS: Var Model, Principal Component Analysis, Random Forest Model

  JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.2, February 24, 2025

  ABSTRACT: 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.