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.
Moghadas, SM, Di Bella, L, Cornelis, B & Munteanu, A 2025, ChronoFusion: Spatio-Temporal Super-Resolution Based on Graph VAEs and Gated Fusion. in 2025 13th European Workshop on Visual Information Processing (EUVIP). IEEE, pp. 1-6, 13th European Workshop on Visual Information Processing, valletta, Malta, 13/10/25. https://doi.org/10.1109/EUVIP66349.2025.11238693, https://doi.org/10.1109/EUVIP66349.2025.11238693
Moghadas, S. M., Di Bella, L., Cornelis, B., & Munteanu, A. (2025). ChronoFusion: Spatio-Temporal Super-Resolution Based on Graph VAEs and Gated Fusion. In 2025 13th European Workshop on Visual Information Processing (EUVIP) (pp. 1-6). IEEE. https://doi.org/10.1109/EUVIP66349.2025.11238693, https://doi.org/10.1109/EUVIP66349.2025.11238693
@inproceedings{5ab8b8206dde4168acf181a0559c8a65,
title = "ChronoFusion: Spatio-Temporal Super-Resolution Based on Graph VAEs and Gated Fusion",
abstract = "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.",
author = "Moghadas, {Seyed Mohamad} and {Di Bella}, Leandro and Bruno Cornelis and Adrian Munteanu",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 13th European Workshop on Visual Information Processing, EUVIP ; Conference date: 13-10-2025 Through 16-10-2025",
year = "2025",
month = nov,
day = "18",
doi = "10.1109/EUVIP66349.2025.11238693",
language = "English",
pages = "1--6",
booktitle = "2025 13th European Workshop on Visual Information Processing (EUVIP)",
publisher = "IEEE",
url = "https://ieeexplore.ieee.org/document/11238693",
}