用频域先验提升时间序列缺失值填补效果
FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation
- 引入可学习的傅里叶偏置模块，显式建模频率特征
- 在高缺失率下优于现有方法，生物数据集上误差降低12.3%
- 适合医疗、交通等含规律缺失模式的数据填补
多变量时间序列填补在医疗、交通和生物建模中至关重要，因传感器故障和不规则采样常导致大量缺失值。现有基于Transformer和扩散模型的方法缺乏显式归纳偏置与频率感知能力，难以应对结构化缺失模式和分布偏移。本文提出FADTI，一种基于扩散框架的方法，通过可学习的傅里叶偏置投影（FBP）模块注入频域先验，并结合自注意力与门控卷积进行时序建模。FBP支持多种谱基，可自适应编码平稳与非平稳模式，将频域归纳偏置融入生成填补过程。在多个基准数据集（包括新提出的生物时间序列数据集）上的实验表明，FADTI在高缺失率下持续超越现有最优方法，尤其在生物数据集上相对误差降低12.3%。代码已公开于https://anonymous.4open.science/r/TimeSeriesImputation-52BF。
原文摘要 · Abstract (English)
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
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