FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911324054749184 |
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| author | Li, Runze Wang, Hanchen Zhang, Wenjie Li, Binghao Zhang, Yu Lin, Xuemin Zhang, Ying |
| author_facet | Li, Runze Wang, Hanchen Zhang, Wenjie Li, Binghao Zhang, Yu Lin, Xuemin Zhang, Ying |
| contents | 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 https://anonymous.4open.science/r/TimeSeriesImputation-52BF |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15116 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation Li, Runze Wang, Hanchen Zhang, Wenjie Li, Binghao Zhang, Yu Lin, Xuemin Zhang, Ying Machine Learning Artificial Intelligence 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 https://anonymous.4open.science/r/TimeSeriesImputation-52BF |
| title | FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2512.15116 |