Multivariate Time Series Imputation (MTSI) is crucial for many applications,such as healthcare monitoring and traffic management, where incomplete data cancompromise decision-making. Existing state-of-the-art methods, like DenoisingDiffusion Probabilistic Models (DDPMs), achieve high imputation accuracy;however, they suffer from significant computational costs and are notablytime-consuming due to their iterative nature. In this work, we propose CoSTI,an innovative adaptation of Consistency Models (CMs) for the MTSI domain. CoSTIemploys Consistency Training to achieve comparable imputation quality to DDPMswhile drastically reducing inference times, making it more suitable forreal-time applications. We evaluate CoSTI across multiple datasets and missingdata scenarios, demonstrating up to a 98% reduction in imputation time withperformance on par with diffusion-based models. This work bridges the gapbetween efficiency and accuracy in generative imputation tasks, providing ascalable solution for handling missing data in critical spatio-temporalsystems.