Transformer encoder for one-step-ahead AQI forecasting using multivariate air-quality monitoring data
Shi-Qi Chen
Short-term forecasting of the Air Quality Index (AQI) can support public health risk management and real-time environmental decision-making. In this study, we propose a multivariate, one-step-ahead time-series forecasting approach based on a Transformer encoder. The model predicts the AQI at the next time point using an observation sequence within a fixed historical window (five time steps in this work), where the input features include AQI and other numerical variables. For data preprocessing,