CoSTI: Consistency Models for (a faster) Spatio-Temporal Imputation
This work addresses efficiency issues in generative imputation for real-time applications like healthcare and traffic management, representing an incremental improvement over existing methods.
The paper tackled the problem of slow inference times in multivariate time series imputation by proposing CoSTI, an adaptation of Consistency Models, which achieved up to a 98% reduction in imputation time while maintaining performance comparable to diffusion-based models.
Multivariate Time Series Imputation (MTSI) is crucial for many applications, such as healthcare monitoring and traffic management, where incomplete data can compromise decision-making. Existing state-of-the-art methods, like Denoising Diffusion Probabilistic Models (DDPMs), achieve high imputation accuracy; however, they suffer from significant computational costs and are notably time-consuming due to their iterative nature. In this work, we propose CoSTI, an innovative adaptation of Consistency Models (CMs) for the MTSI domain. CoSTI employs Consistency Training to achieve comparable imputation quality to DDPMs while drastically reducing inference times, making it more suitable for real-time applications. We evaluate CoSTI across multiple datasets and missing data scenarios, demonstrating up to a 98% reduction in imputation time with performance on par with diffusion-based models. This work bridges the gap between efficiency and accuracy in generative imputation tasks, providing a scalable solution for handling missing data in critical spatio-temporal systems.