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

1. 1Collect and preprocess multi-year pollutant and meteorological time-series data
2. 2Implement and compare different lightweight and interpretable forecasting models
3. 3Evaluate model performance using metrics such as accuracy and computational efficiency
4. 4Select the best-performing model for hourly PM2.5 prediction in urban areas

Data scientists and researchers on a team can benefit from this study to develop more accurate and efficient air quality forecasting models, while policymakers can use the insights to inform urban management decisions

💡 Lightweight and interpretable forecasting approaches can provide competitive performance for hourly PM2.5 prediction

🌟 Interpretable models for PM2.5 forecasting can match complex models' performance! 📊