AdaSTI: Conditional Diffusion Models with Adaptive Dependency Modeling for Spatio-Temporal Imputation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866915506912493568 |
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| author | Yang, Yubo Zhu, Yichen Jiang, Bo |
| author_facet | Yang, Yubo Zhu, Yichen Jiang, Bo |
| contents | Spatio-temporal data abounds in domain like traffic and environmental monitoring. However, it often suffers from missing values due to sensor malfunctions, transmission failures, etc. Recent years have seen continued efforts to improve spatio-temporal data imputation performance. Recently diffusion models have outperformed other approaches in various tasks, including spatio-temporal imputation, showing competitive performance. Extracting and utilizing spatio-temporal dependencies as conditional information is vital in diffusion-based methods. However, previous methods introduce error accumulation in this process and ignore the variability of the dependencies in the noisy data at different diffusion steps. In this paper, we propose AdaSTI (Adaptive Dependency Model in Diffusion-based Spatio-Temporal Imputation), a novel spatio-temporal imputation approach based on conditional diffusion model. Inside AdaSTI, we propose a BiS4PI network based on a bi-directional S4 model for pre-imputation with the imputed result used to extract conditional information by our designed Spatio-Temporal Conditionalizer (STC)network. We also propose a Noise-Aware Spatio-Temporal (NAST) network with a gated attention mechanism to capture the variant dependencies across diffusion steps. Extensive experiments on three real-world datasets show that AdaSTI outperforms existing methods in all the settings, with up to 46.4% reduction in imputation error. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18144 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AdaSTI: Conditional Diffusion Models with Adaptive Dependency Modeling for Spatio-Temporal Imputation Yang, Yubo Zhu, Yichen Jiang, Bo Machine Learning Artificial Intelligence Spatio-temporal data abounds in domain like traffic and environmental monitoring. However, it often suffers from missing values due to sensor malfunctions, transmission failures, etc. Recent years have seen continued efforts to improve spatio-temporal data imputation performance. Recently diffusion models have outperformed other approaches in various tasks, including spatio-temporal imputation, showing competitive performance. Extracting and utilizing spatio-temporal dependencies as conditional information is vital in diffusion-based methods. However, previous methods introduce error accumulation in this process and ignore the variability of the dependencies in the noisy data at different diffusion steps. In this paper, we propose AdaSTI (Adaptive Dependency Model in Diffusion-based Spatio-Temporal Imputation), a novel spatio-temporal imputation approach based on conditional diffusion model. Inside AdaSTI, we propose a BiS4PI network based on a bi-directional S4 model for pre-imputation with the imputed result used to extract conditional information by our designed Spatio-Temporal Conditionalizer (STC)network. We also propose a Noise-Aware Spatio-Temporal (NAST) network with a gated attention mechanism to capture the variant dependencies across diffusion steps. Extensive experiments on three real-world datasets show that AdaSTI outperforms existing methods in all the settings, with up to 46.4% reduction in imputation error. |
| title | AdaSTI: Conditional Diffusion Models with Adaptive Dependency Modeling for Spatio-Temporal Imputation |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.18144 |