[2603.25495] Interpretable PM2.5 Forecasting for Urban Air Quality: A Comparative Study of Operational Time-Series Models

Computer Science > Machine Learning arXiv:2603.25495 (cs) [Submitted on 26 Mar 2026] Title:Interpretable PM2.5 Forecasting for Urban Air Quality: A Comparative Study of Operational Time-Series Models Authors:Moazzam Umer Gondal, Hamad ul Qudous, Asma Ahmad Farhan, Sultan Alamri View a PDF of the paper titled Interpretable PM2.5 Forecasting for Urban Air Quality: A Comparative Study of Operational Time-Series Models, by Moazzam Umer Gondal and 3 other authors View PDF HTML (experimental) Abstract:Accurate short-term air-quality forecasting is essential for public health protection and urban management, yet many recent forecasting frameworks rely on complex, data-intensive, and computationally demanding models. This study investigates whether lightweight and interpretable forecasting approaches can provide competitive performance for hourly PM2.5 prediction in Beijing, China. Using multi-year pollutant and meteorological time-series data, we developed a leakage-aware forecasting workflow that combined chronological data partitioning, preprocessing, feature selection, and exogenous-driver modeling under the Perfect Prognosis setting. Three forecasting families were evaluated: SARIMAX, Facebook Prophet, and NeuralProphet. To assess practical deployment behavior, the models were tested under two adaptive regimes: weekly walk-forward refitting and frozen forecasting with online residual correction. Results showed clear differences in both predictive accuracy and computational ef...