T1: One-to-One Channel-Head Binding for Multivariate Time-Series Imputation

• URL: http://arxiv.org/abs/2602.21043v1
• Date: Tue, 24 Feb 2026 16:03:46 GMT
• Title: T1: One-to-One Channel-Head Binding for Multivariate Time-Series Imputation
• Abstract summary: Imputing missing values in time series is challenging under diverse missing patterns and heavy missingness.<n>We introduce T1 (Time series imputation with 1-to-1 channel-head binding), a CNN-Transformer hybrid architecture that achieves robust imputation.<n>T1 achieves state-of-the-art performance, reducing MSE by 46% on average compared to the second-best baseline.
• Score: 6.002503434201551
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Imputing missing values in multivariate time series remains challenging, especially under diverse missing patterns and heavy missingness. Existing methods suffer from suboptimal performance as corrupted temporal features hinder effective cross-variable information transfer, amplifying reconstruction errors. Robust imputation requires both extracting temporal patterns from sparse observations within each variable and selectively transferring information across variables--yet current approaches excel at one while compromising the other. We introduce T1 (Time series imputation with 1-to-1 channel-head binding), a CNN-Transformer hybrid architecture that achieves robust imputation through Channel-Head Binding--a mechanism creating one-to-one correspondence between CNN channels and attention heads. This design enables selective information transfer: when missingness corrupts certain temporal patterns, their corresponding attention pathways adaptively down-weight based on remaining observable patterns while preserving reliable cross-variable connections through unaffected channels. Experiments on 11 benchmark datasets demonstrate that T1 achieves state-of-the-art performance, reducing MSE by 46% on average compared to the second-best baseline, with particularly strong gains under extreme sparsity (70% missing ratio). The model generalizes to unseen missing patterns without retraining and uses a consistent hyperparameter configuration across all datasets. The code is available at https://github.com/Oppenheimerdinger/T1.

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