Dual-Branch Transformer with Temporal-Feature Fusion for Multivariate Time Series Imputation

Multivariate time series data often suffer from missing values due to external factors such as sensor failures and data transmission errors. To address these challenges, this paper proposes a dual-branch spatiotemporal imputation model based on Transformer, named DBTF. The model captures both temporal and feature correlations through a dual-branch architecture. Specifically, the parallel temporal and feature paths are designed to extract long-range temporal dependencies and cross-feature spatial correlations, respectively. A cross-attention fusion module is introduced to enable adaptive interaction between the two paths. The feature branch leverages a patch-based representation combined with a feature attention mechanism to model complex inter-feature correlations. In parallel, the temporal branch employs a novel Projected Attention Embedding (PAE) module to enhance long-range temporal dependency modeling. The fusion module employs a gated cross-attention mechanism to dynamically adjust the importance weights of temporal and feature information. Finally, a multi-layer perceptron with residual connections is used as the prediction head to improve imputation accuracy. Experimental results on multiple real-world datasets demonstrate that DBTF can effectively complete missing data and exhibits strong robustness under various missing rates. This study provides an effective solution for accurate recovery of multivariate time series with complex missing patterns.