A Patch-Based Multiple Imputation Transformer Model for Time Series Data Completion

In the real world, collecting time series data often encounters difficulties and uncertainties, leading to prevalent data missing issues. The time series imputation task aims to restore data integrity and improve prediction performance. Existing models mainly use single imputation to handle missing data. This paper proposes a Patch-based Multiple Imputation Transformer (PMIT), which learns data correlations within the same feature through patch operations and channel-independent (CI) operations. The model employs multiple imputation training, optimizing imputed data propagation across different modules, and incorporates attention mechanisms and dynamic adjustment methods to address complex dependencies. Experimental results show that PMIT outperforms recent models in imputation performance and generalization ability across various realworld datasets.