Long-term interpretable air quality trend forecasting via directed interval fuzzy cognitive maps

Accurate air quality forecasting is crucial for public health and addressing air pollution. However, the dynamic evolution trends, the cross-interference among different air quality indexes, and the error accumulation in the long-term prediction process are still open problems when establishing air quality forecasting models. Thus, we present a long-term interpretable air quality trend forecasting model to address these challenges via directed interval fuzzy cognitive maps, DE-DIFCM. Specifically, we design a time series trend extraction and representation learning module based on the interval fuzzy granules and the Cramer decomposition theorem in the first phase. Next, we formulate the interval information granules' time series forecasting as a directed interval fuzzy cognitive map (DIFCM). In particular, we employ PM2.5 as a benchmark to validate the performance of the proposed DE-DIFCM. Experimental results on six air quality monitoring datasets demonstrate the model's superior and competitive long-term prediction performance by comparison with some representative baselines.