Diffusion models have demonstrated strong performance in time series modeling due to their ability to progressively capture complex data distributions through iterative denoising. However, existing approaches struggle with frequency-sensitive denoising, high-frequency reconstruction and balancing global trends with local dynamics. To address these limitations, we propose \textbf{HyFAD}, a \textbf{Hy}brid time-frequency \textbf{D}iffusion model with \textbf{F}requency-\textbf{A}ware embedding for time series imputation. Built upon the DDPM paradigm, HyFAD adopts a coupled time-frequency diffusion framework, in which the reverse denoising proceeds sequentially from the time domain to the frequency domain, enabling coarse-to-fine generation. Specifically, the time-domain diffusion process captures low-frequency global trends, while the frequency-domain diffusion process refines high-frequency spectral components. We further introduce a frequency-aware step embedding that exploits the relationship between diffusion steps and spectral components, providing step-dependent spectral guidance and facilitates more accurate band-wise reconstruction. Extensive experiments on multiple benchmark datasets demonstrate that HyFAD achieves state-of-the-art performance. Our source code is available at https://github.com/hongfangao/HyFAD.

翻译：扩散模型凭借其通过迭代去噪逐步捕捉复杂数据分布的能力，在时间序列建模中展现出优异性能。然而，现有方法在频率敏感去噪、高频重建以及平衡全局趋势与局部动态方面仍存在不足。为解决这些局限，我们提出**HyFAD**——一种基于频率感知嵌入的**混合**时频**扩散**模型，专门用于时间序列填充。该模型以DDPM范式为基础，采用耦合的时频扩散框架，其中反向去噪过程从时域到频域依次推进，实现从粗到细的生成。具体而言，时域扩散过程捕捉低频全局趋势，而频域扩散过程则细化高频频谱分量。我们进一步引入频率感知步长嵌入，利用扩散步长与频谱分量之间的关联，提供依赖步长的频谱引导，从而支持更精确的波段重建。在多个基准数据集上的大量实验表明，HyFAD达到了最先进的性能。我们的源代码已在https://github.com/hongfangao/HyFAD公开。