HSIC-DIMFMC: A Multi-View Functional Matrix Completion Method with Dual-Information Graph Regularization for Meteorological Data Imputation

Continuous and complete meteorological observations are essential for reliable climate analysis and environmental assessment. However, missing values caused by sensor malfunctions and transmission failures can introduce systematic biases and increase uncertainty in downstream applications. Meteorological variables can be modeled as functional data and typically exhibit nonlinear inter-variable dependencies alongside temporal smoothness; these properties provide valuable prior information for missing data recovery. To address this issue, we propose HSIC-DIMFMC, a multi-view functional matrix completion method for meteorological data imputation that integrates the Hilbert–Schmidt Independence Criterion (HSIC) and dual-information graph regularization. Within a unified framework of functional data analysis and multi-view learning, HSIC is utilized to capture nonlinear dependencies across multiple views, while dual-information graph regularization preserves local structural relationships and temporal smoothness. This joint modeling strategy significantly improves latent representation learning and enhances imputation performance. Experiments on real meteorological datasets demonstrate that the proposed method consistently outperforms several state-of-the-art baselines, especially for strongly correlated variable pairs such as temperature–dew point and wind speed–maximum wind speed. Compared with seven representative approaches—ranging from traditional spatial interpolation to advanced functional matrix completion models—HSIC-DIMFMC achieves average reductions of 43.97–73.59% in RMSE. The results indicate that HSIC-DIMFMC effectively exploits nonlinear cross-view dependencies and structural information, providing a robust solution for collaborative meteorological data imputation.