SFDformer: a frequency-based sparse decomposition transformer for air pollution time series prediction

Introduction: With the rapid advancement of industrialization and the prevalent occurrence of haze weather, (Formula presented.) contamination has emerged asa significant threat to public health and environmental sustainability. The concentration of (Formula presented.) exhibits intricate dynamic attributes and is profoundly correlated with meteorological conditions as well as the concentrations of other pollutants, thereby substantially augmenting the complexity of predictive endeavors. Methods: A novel predictive methodology has been developed. It integrates time seriesfrequency domain analysis with the decomposition of deep learning models. This approach facilitates the capture of interdependencies among high - dimensional features through time series decomposition, employs Fourier Transform to mitigate noise interference, and incorporates sparse attention mechanisms to selectively filter critical frequency components, thereby enhancing time - dependent modeling. Importantly, this technique effectively reduces computational complexity from (Formula presented.) to (Formula presented.). Results: Empirical findings substantiate that this methodology yields notably superior predictive accuracy relative to conventional models across a diverse array of real-world datasets. Discussion: This advancement not only offers an efficacious resolution for (Formula presented.) prediction tasks but also paves the way for innovative research and application prospects in the realm of complex time series modeling.