FADTI: 基于傅里叶和注意力驱动的多变量时间序列插补扩散模型
FADTI: Fourier and Attention Driven Diffusion for Multivariate Time Series Imputation
AI总结：
提出FADTI扩散框架，通过可学习傅里叶偏置投影模块注入频域归纳偏置，结合自注意力与门控卷积进行时序建模，在多个基准上优于现有方法，尤其在高缺失率下表现突出。
摘要：
多变量时间序列插补是医疗保健、交通预测和生物建模等应用中的基础问题，其中传感器故障和不规则采样导致普遍存在的缺失值。然而，现有的基于Transformer和扩散的模型缺乏明确的归纳偏置和频率感知，限制了它们在结构化缺失模式和分布偏移下的泛化能力。我们提出FADTI，一个基于扩散的框架，通过可学习的傅里叶偏置投影（FBP）模块注入频率信息特征调制，并将其与通过自注意力和门控卷积进行的时间建模相结合。FBP支持多种谱基，能够自适应编码平稳和非平稳模式。这种设计将频域归纳偏置注入生成式插补过程。在多个基准（包括一个新引入的生物时间序列数据集）上的实验表明，FADTI持续优于最先进的方法，尤其是在高缺失率下。代码可在该https URL获取。
英文摘要：
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. Existing Transformer- and diffusion-based imputers achieve strong performance, but they often rely mainly on time-domain modeling and lack adaptive spectral bias for recovering structured temporal gaps. We propose FADTI, a Fourier- and attention-driven diffusion framework for multivariate time series imputation. FADTI introduces a Fourier Bias Projection (FBP) module that injects learnable frequency-aware bias into intermediate hidden states during denoising. It projects intermediate hidden states onto Fourier bases, avoiding direct spectral estimation from masked or zero-filled inputs. With DFT, STFT, and FSST instantiations, FBP captures global periodicity, localized time--frequency variations, and non-stationary oscillatory patterns. By coupling FBP with self-attention and gated convolution, FADTI integrates frequency-domain guidance, temporal dependency modeling, and probabilistic denoising in a unified framework. Experiments on multiple benchmarks, including a new biological imputation benchmark, show that FADTI improves accuracy, uncertainty estimation, and sampling efficiency, especially under high missing rates and structured missing patterns. Code is available at https://github.com/RazeenLI/FADTI
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
