SFDformer: a frequency-based sparse decomposition transformer for air pollution time series prediction
Year: 2025
Type: article
Abstract: Introduction With the rapid advancement of industrialization and the prevalent occurrence of haze weather, P M 2.5 contamination has emerged asa significant threat to public health and environmental sustainability. The concentration of P M 2.5 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 O ( L 2 ) to O ( L log L ) . 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 P M 2.5 prediction tasks but also paves the way for innovative research and application prospects in the realm of complex time series modeling.
Authors: Zhenkai Qin, Baozhong Wei, Caifeng Gao, Xiaolong Chen, Hongfeng Zhang, Cora Un In Wong
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4408535576