Cross-Domain Conditional Diffusion Models for Time Series Imputation

TL;DR

This paper introduces a novel cross-domain diffusion model for time series imputation, effectively handling high missing rates and domain shifts by combining spectral interpolation, domain-shared representation learning, and consistency alignment.

Contribution

It proposes a new diffusion-based framework with spectral interpolation and cross-domain consistency strategies specifically designed for cross-domain time series imputation.

Findings

Outperforms existing methods on three real-world datasets.

Effectively captures domain-shared and domain-specific temporal features.

Handles high missing rates and domain shifts successfully.

Abstract

Cross-domain time series imputation is an underexplored data-centric research task that presents significant challenges, particularly when the target domain suffers from high missing rates and domain shifts in temporal dynamics. Existing time series imputation approaches primarily focus on the single-domain setting, which cannot effectively adapt to a new domain with domain shifts. Meanwhile, conventional domain adaptation techniques struggle with data incompleteness, as they typically assume the data from both source and target domains are fully observed to enable adaptation. For the problem of cross-domain time series imputation, missing values introduce high uncertainty that hinders distribution alignment, making existing adaptation strategies ineffective. Specifically, our proposed solution tackles this problem from three perspectives: (i) Data: We introduce a frequency-based time series interpolation strategy that integrates shared spectral components from both domains while retaining domain-specific temporal structures, constructing informative priors for imputation. (ii) Model: We design a diffusion-based imputation model that effectively learns domain-shared representations and captures domain-specific temporal dependencies with dedicated denoising networks. (iii) Algorithm: We further propose a cross-domain consistency alignment strategy that selectively regularizes output-level domain discrepancies, enabling effective knowledge transfer while preserving domain-specific characteristics. Extensive experiments on three real-world datasets demonstrate the superiority of our proposed approach. Our code implementation is available here.