EvaNet: An Extreme Value Attention Network for Long-Term Air Quality Prediction

Air quality affects social activities and human health. Air quality prediction, especially for extreme events such as severe haze pollution, plays an essential guiding role in government decision-making and outdoor activity scheduling. Established prediction models face the challenges of forecasting extreme values and long-term tendency. In this paper, we propose an extreme value attention network (EvaNet) based on encoder and decoder framework to achieve long-term air quality prediction. This model designs an extreme value attention mechanism to alleviate the impact of sudden changes on prediction. In addition, to capture long-term dependence relationships, EvaNet introduces a temporal attention mechanism. Integrating the dual attention mechanisms, the extracted features are fed into a decoder to yield the final prediction. The experiments evaluated on two real-world air quality datasets show the superiority of our method against other state-of-the-art baselines.