SH-Imputer: Spatiotemporal Data Imputation under Sparse Historical Data for Sparse Sensing

Abstract

Sparse sensing is a critical task in many real-world systems, heavily relying on effective data imputation. However, most existing methods overlook the severe sparsity of historical data available for training, which poses a fundamental challenge to achieving accurate imputation. Intrinsic priors-based models (i.e., MF, ODEs) are robust to sparsity but have limited performance with abundant historical data, whereas deep learning models excel with rich data but fail dramatically when data are sparse. Furthermore, this data sparsity introduces significant solution uncertainty and creates a need for more computationally efficient dependency modeling. To address these, we propose SH-Imputer, a novel adaptive framework. It first employs our proposed VDMF and AVDCDE modules to model the uncertainty of intrinsic spatiotemporal properties. Subsequently, a lightweight ST-Mamba module efficiently learns complex spatiotemporal dependencies. The entire process is governed by an adaptive mechanism that balances prior-based robustness with data-driven expressiveness, ensuring superior performance under varying historical data sparsity. We also provide theoretical justifications for our core design choices. Extensive experiments validate that SH-Imputer significantly outperforms state-of-the-art methods. Our code is available at https://github.com/JLUDhhh/SH-Imputer.