GAUDI: Geometry-Aware Diffusion for Calibrated Air-Quality Time-Series Imputation

A paper submitted to arXiv on 24 Sep 2026 introduces GAUDI, a geometry-aware conditional diffusion imputer for air-quality time-series data that achieves an RMSE of 0.340 on the ItalyAir benchmark, compared with 0.355 for full context and 0.355 for local CSDI. The configuration retains temporal and feature processing, visible-value and mask conditioning, variable identity, and diffusion-step information while suppressing absolute time-position side embeddings, evaluated on 13 variables, length-32 windows, and nominal 50% block missingness across three archived seeds. The authors state the experiment isolates a geometry-aware conditioning effect under block missingness.

Computer Science Machine Learning Submitted on 24 Sep 2026 Title:GAUDI: Geometry-Aware Diffusion for Calibrated Air-Quality Time-Series Imputation View PDF https://arxiv.org/pdf/2609.30340 HTML experimental https://arxiv.org/html/2609.30340v1 Abstract:Air-quality sensor outages often create contiguous missing blocks, where side information useful for isolated missingness may be less reliable. We study a block-specific, GAUDI-aligned conditional diffusion imputer that retains temporal and feature processing, visible-value and mask conditioning, variable identity, and diffusion-step information, while suppressing absolute time-position side embeddings. On ItalyAir 13 variables, length-32 windows, nominal 50% block missingness; three archived seeds , this feature-side configuration achieves RMSE 0.340, versus 0.355 for full context and 0.355 for local CSDI. The experiment isolates a geometry-aware conditioning effect under block missingness. References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer What is the Explorer? https://info.arxiv.org/labs/showcase.html arxiv-bibliographic-explorer Connected Papers What is Connected Papers? https://www.connectedpapers.com/about Litmaps What is Litmaps? https://www.litmaps.co/ scite Smart Citations What are Smart Citations? https://www.scite.ai/ Code, Data and Media Associated with this Article alphaXiv What is alphaXiv? https://alphaxiv.org/ CatalyzeX Code Finder for Papers What is CatalyzeX? https://www.catalyzex.com DagsHub What is DagsHub? https://dagshub.com/ Gotit.pub What is GotitPub? http://gotit.pub/faq Hugging Face What is Huggingface? https://huggingface.co/huggingface ScienceCast What is ScienceCast? https://sciencecast.org/welcome Demos Recommenders and Search Tools Influence Flower What are Influence Flowers? https://influencemap.cmlab.dev/ CORE Recommender What is CORE? https://core.ac.uk/services/recommender IArxiv Recommender What is IArxiv? https://iarxiv.org/about arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs https://info.arxiv.org/labs/index.html .