ChronoFusion: Spatio-Temporal Super-Resolution Based on Graph VAEs and Gated Fusion

Time series data often suffers from resolution limitations due to hardware constraints, sampling frequency restrictions, or economic considerations. While super-resolution techniques have seen significant advancements in computer vision, their application to spatio-temporal data presents unique challenges that remain under-explored. We argue that pure generative or auto-regressive approaches are subpar for the multi-modal super-resolution task. Hence, we introduce ChronoFusion, a novel hybrid model that simultaneously enhances both spatial and temporal resolution of time series data. Our approach leverages a graph variational autoencoder combined with adaptive attention mechanisms to generate high-resolution time series from low-resolution inputs. Unlike previous methods that handle spatial and temporal super-resolution separately, ChronoFusion integrates both dimensions through a proxy subspace. Extensive evaluation on traffic datasets in various locations demonstrates that ChronoFusion outperforms state-of-the-art methods by 10% on average in interpolation fidelity on unseen nodes while maintaining temporal consistency. Furthermore, our model demonstrates strong capabilities in handling missing data. The method's versatility across diverse spatio-temporal traffic applications makes it a valuable contribution to time series analysis and modeling. Github Repo