Abstract
Time series imputation has progressed from statistical and deep learning approaches to diffusion-based models, which have shown strong recent performance. Existing diffusion-based methods typically condition the reverse process using local contextual information from the current or neighbouring windows. Meanwhile, global dataset-level structure often remains implicit, limiting performance when local observations are sparse, noisy, or unrepresentative. To address this issue, we propose ProCTI, a diffusion-imputation framework that augments local conditioning with retrieved global dataset-level priors through learned prototypes. A hybrid conditioning mechanism integrates this global context with local signals during reverse diffusion, enabling more accurate reconstruction under varying missingness scenarios. Experiments across multiple benchmark datasets show that ProCTI outperforms strong baselines overall under random missingness, while remaining competitive under attribute-wise missingness. Furthermore, we use a latent-regime data model to characterise the precise conditions under which prototype-derived global conditioning provably improves imputation. We support this with a general theoretical analysis of local-global conditioning.
Keywords
Subject
Publication details
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- Open access
- Green open access
Cite this article
APA 7
Rashid, F., Van Le, D., Masood, R., Batista, G., Seneviratne, A., & Seneviratne, S. (2026). ProCTI: Prototype-Refined Global Conditioning for Diffusion-Based Time Series Imputation. https://omanscience.com/en/articles/procti-prototype-refined-global-conditioning-for-diffusion-based-time-series-imputation
MLA 9
Rashid, Fariza, et al. "ProCTI: Prototype-Refined Global Conditioning for Diffusion-Based Time Series Imputation." https://omanscience.com/en/articles/procti-prototype-refined-global-conditioning-for-diffusion-based-time-series-imputation.
Chicago (author–date)
Rashid, Fariza, Duc Van Le, Rahat Masood, Gustavo Batista, Aruna Seneviratne, and Suranga Seneviratne. 2026. "ProCTI: Prototype-Refined Global Conditioning for Diffusion-Based Time Series Imputation." https://omanscience.com/en/articles/procti-prototype-refined-global-conditioning-for-diffusion-based-time-series-imputation.
Harvard
Rashid, F., Van Le, D., Masood, R., Batista, G., Seneviratne, A. and Seneviratne, S. (2026) 'ProCTI: Prototype-Refined Global Conditioning for Diffusion-Based Time Series Imputation', Available at: https://omanscience.com/en/articles/procti-prototype-refined-global-conditioning-for-diffusion-based-time-series-imputation.
Vancouver
Rashid F, Van Le D, Masood R, Batista G, Seneviratne A, Seneviratne S. ProCTI: Prototype-Refined Global Conditioning for Diffusion-Based Time Series Imputation. https://omanscience.com/en/articles/procti-prototype-refined-global-conditioning-for-diffusion-based-time-series-imputation
IEEE
F. Rashid, D. Van Le, R. Masood, G. Batista, A. Seneviratne, and S. Seneviratne, "ProCTI: Prototype-Refined Global Conditioning for Diffusion-Based Time Series Imputation," https://omanscience.com/en/articles/procti-prototype-refined-global-conditioning-for-diffusion-based-time-series-imputation.