FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation

Abstract:Multivariate time series imputation is fundamental in applications such as healthcare, traffic forecasting, and biological modeling, where sensor failures and irregular sampling lead to pervasive missing values. However, existing Transformer- and diffusion-based models lack explicit inductive biases and frequency awareness, limiting their generalization under structured missing patterns and distribution shifts. We propose FADTI, a diffusion-based framework that injects frequency-informed feature modulation via a learnable Fourier Bias Projection (FBP) module and combines it with temporal modeling through self-attention and gated convolution. FBP supports multiple spectral bases, enabling adaptive encoding of both stationary and non-stationary patterns. This design injects frequency-domain inductive bias into the generative imputation process. Experiments on multiple benchmarks, including a newly introduced biological time series dataset, show that FADTI consistently outperforms state-of-the-art methods, particularly under high missing rates. Code is available at this https URL

This work has been submitted to the IEEE for possible publication. 10 pages, 7 figures

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2512.15116 [cs.LG]

(or arXiv:2512.15116v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2512.15116 Focus to learn more arXiv-issued DOI via DataCite

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