TFITS: Time Series Imputation via Dual-Perspective Fusion of Temporal and Feature Views

In the field of multivariate time series analysis, data completeness plays a crucial role in ensuring the accuracy and reliability of downstream tasks. However, in practical applications, factors such as measurement errors and equipment failures often lead to partial missing values in the data. Therefore, this paper introduces a novel model, focusing on how to efficiently handle missing values in multivariate time series and mitigate the potential negative impact of data incompleteness on subsequent tasks. In previous studies, researchers often adopted a single-perspective approach, separately considering the influence of the temporal dimension and the feature dimension, thereby overlooking the potential of dual-perspective fusion. This paper proposes TFITS, an innovative method for predicting missing values in multivariate time series data. TFITS approaches the problem from both the temporal and feature perspectives of time series data, leveraging the UNetFuse module to fuse feature maps generated from these two perspectives, thereby providing a more comprehensive solution to the missing value problem. Through this approach, TFITS can more effectively address missing values in multivariate time series data. Experimental results demonstrate that the TFITS model not only achieves excellent imputation performance but also exhibits superior and stable performance under different missing rate conditions.