Time-Frequency Memory-Guided Diffusion for Generative Time Series Imputation

Score-based diffusion models have shown strong performance in probabilistic time series imputation. However, their performance can degrade under extreme sparsity, where limited observations provide insufficient context to reliably guide the reverse diffusion process. Consequently, while the generated imputations may exhibit plausible local patterns, they often fail to adequately preserve the broader temporal structures aligned with the observed context. To address this issue, we propose Time-Frequency Memory-Guided Diffusion (TFMemDiff), a unified framework that augments conditional diffusion with complementary memory priors in both the temporal and frequency domains. Rather than treating memory vectors as auxiliary features to be simply concatenated with diffusion inputs, TFMemDiff uses memory as an external structural reference that guides conditional score estimation throughout the reverse diffusion process. Specifically, TFMemDiff incorporates a Frequency Memory Module (FMM) to retrieve recurring global spectral patterns and a Temporal Memory Module (TMM) to capture localized temporal dependencies. These memories distill reusable structural knowledge from historical training sequences and make it accessible to each incomplete instance during denoising. Extensive experiments on multiple real-world datasets demonstrate the effectiveness of TFMemDiff across diverse missingness settings, showing that memory-guided structural priors can substantially improve generative imputation under severe sparsity.