Discretizing Continuous Time Series for Imputation with Masked Diffusion Training
Time series imputation is a crucial area for reliable time series analysis, yet it remains challenging due to the complex temporal dynamics and noise of real-world data. Existing approaches, however, exhibit two limitations: missing and observed values are embedded within the same representation space without explicit structural separation, and continuous diffusion-based methods are trained to predict added noise rather than the original signal. To address these, we propose the Masked Diffusion Time-series Imputation Model (MDTIM), which leverages the training paradigm of masked diffusion mode
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- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-xl-base-1.0 →
“Fuzzy title match (0.73): “Discretizing Continuous Time Series for Imputation with Mask” ≈ “stabilityai/stable-diffusion-xl-base-1.0””
- FuzzySimilar title/name (fuzzy) · 59%CompVis/stable-diffusion-v1-4 →
“Fuzzy title match (0.73): “Discretizing Continuous Time Series for Imputation with Mask” ≈ “CompVis/stable-diffusion-v1-4””
- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-3.5-large →
“Fuzzy title match (0.73): “Discretizing Continuous Time Series for Imputation with Mask” ≈ “stabilityai/stable-diffusion-3.5-large””
- PossiblePossibly related (embedding) · 47%Diffusion →
- FuzzyOverlapping authors or contributors · 62%browser-use/browser-use →
“Shared author/contributor keys: lee”
- FuzzyOverlapping authors or contributors · 62%microsoft/ML-For-Beginners →
“Shared author/contributor keys: shin”
- LinkedLinked via arxiv author · 85%Dongbin Kim →
“Discretizing Continuous Time Series for Imputation with Masked Diffusion Training”
- LinkedLinked via arxiv author · 85%Seungyun Lee →
“Discretizing Continuous Time Series for Imputation with Masked Diffusion Training”