sasdim: self-adaptive noise scaling diffusion model for spatial time series imputation

Understand

Spatial time series imputation is critically important to many real applications such as intelligent transportation and air quality monitoring.

• Although recent transformer and diffusion model based approaches have achieved significant performance gains compared with conventional statistic based methods, spatial time series imputation still remains as a challenging issue due to the complex spatio-temporal dependencies and the noise uncertainty of the spatial time series data.
• Especially, recent diffusion process based models may introduce random noise to the imputations, and thus cause negative impact on the model performance.
• To this end, we propose a self-adaptive noise scaling diffusion model named SaSDim to more effectively perform spatial time series imputation.