A conditional generative diffusion model for spatio-temporal data

Diffusion models have become widely used for generating text, image, video, and audio. In recent years, these models have also been introduced into the time series domain for tasks such as forecasting, imputation, and generation. An interesting application is conditional generation with respect to metadata, enabling the synthesis of data sequences that match specified conditions. In the present work, we focus on spatio-temporal data and more precisely on time series data that also exhibit a spatial nature - for instance, measurements from sensor networks, traffic flows, mobile network usage across different areas. However, current approaches for time series conditional generation often neglect spatial autocorrelation. In this work, we extend the well-known DIFFWAVE model to address this challenge by directly taking into account the spatial nature of the data in the denoising process of the diffusion model. We evaluate our approach on a large and complex real-world dataset from the NET-MOB 2023 data challenge, which collects mobile network usage of different mobile applications across urban areas. Our results demonstrate that, in addition to achieving competitive performance across all evaluated metrics, our approach is also able to correctly capture the spatial autocorrelation present in the real data.