  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. Specially, we propose a new loss function that can scale the noise
to the similar intensity, and propose the across spatial-temporal global
convolution module to more effectively capture the dynamic spatial-temporal
dependencies. Extensive experiments conducted on three real world datasets
verify the effectiveness of SaSDim by comparison with current state-of-the-art
baselines.
