FADTI: Fourier and Attention-Based Diffusion Model for Multivariate Time Series Imputation
Researchers have developed FADTI, a diffusion-based framework that combines Fourier-based frequency modulation with attention mechanisms to fill missing values in multivariate time series data. The method addresses limitations in existing Transformer and diffusion models by incorporating explicit frequency-domain inductive bias through a learnable Fourier Bias Projection module. The approach shows consistent improvements over state-of-the-art methods, particularly when dealing with high rates of missing data in applications like healthcare and traffic forecasting.
FADTI is a novel diffusion-based imputation framework designed to handle missing values in multivariate time series, a common problem in domains such as healthcare, traffic forecasting, and biological modeling where sensor failures and irregular sampling occur. The key innovation is the integration of frequency-informed feature modulation via a Fourier Bias Projection (FBP) module that supports multiple spectral bases, enabling the model to adaptively encode both stationary and non-stationary patterns. The framework combines this frequency-domain approach with temporal modeling through self-attention and gated convolution mechanisms. Experimental validation across multiple benchmarks, including a newly introduced biological time series dataset, demonstrates that FADTI consistently outperforms existing state-of-the-art methods, with particularly strong results under high missing data rates. The authors have made code publicly available to support reproducibility.
What's missing
The paper is a preprint submitted to IEEE for possible publication and has not yet undergone peer review. Computational complexity analysis, runtime comparisons with baseline methods, and details on the specific biological time series dataset introduced are not provided in the abstract.
What different sources said
- arXiv cs.AICenter
FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation
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