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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.

by read1 min views1 publishedSep 29, 2026
GAUDI: Geometry-Aware Diffusion for Calibrated Air-Quality Time-Series Imputation
Image: source
  [Submitted on 24 Sep 2026]


[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.

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