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arXiv AI By Dongbin Kim, Seungyun Lee, Geonwoo Shin, Jaewook Lee

Discretizing Continuous Time Series for Imputation with Masked Diffusion Training

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The paper introduces the Masked Diffusion Time-series Imputation Model (MDTIM), which uses a masked diffusion training paradigm to directly predict original values for time series imputation. It separates missing and observed data via a MASK token and employs Stochastic Discretization to convert continuous values into ordinal-aware tokens, preserving temporal dynamics. Experiments on multiple benchmarks show that MDTIM outperforms existing deterministic and generative baselines in robustness and scalability across various missing data scenarios.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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