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

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.

Machine Learning (cs.LG)

arXiv:2609.30340 [cs.LG]

(or arXiv:2609.30340v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.30340 Focus to learn more arXiv-issued DOI via DataCite

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