Semiparametric hybrid air quality forecasting using prophet ensemble and feature selection

Due to air quality management and public health planning, accurately predicting PM2.5 concentrations is absolutely imperative. Having many lagged variables, interactions between pollutants and other temporal features means that high-dimensional environmental datasets (that may include multiple features) may hinder forecasting performance. To mitigate this problem, we develop a hybrid forecasting framework combining the Prophet time series model with ensemble-based machine learning techniques for residual modelling. Residuals from prophet are modelled via bagging, boosting, stacking and voting regressors. To cope high-dimensionality, feature selection techniques such as Lasso, Shap and Recursive Feature Elimination, amongst others are applied such that the remaining influential features may be accurately modelled and interpreted. As benchmarks, the hybrids are also tested against stand along machine learning models like XGboost, SVR and LSTM' s. Forecasting with the models is performed on both a high- dimensional simulated dataset and real daily air quality data from Seoul, South Korea. We found that the prophet with boosting ensemble-based model coupled with Lasso and Shap have lowest errors based on RMSE, compared to Prophet with other machine learning ensemble residual regressors and also standalone models in high-dimensional settings. We offer a statistically sound, interpretable and scalable solution for forecasting air pollution in high dimensions.