ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908882598625280 |
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| author | Liu, Xvyuan Qiu, Xiangfei Cheng, Hanyin Wu, Xingjian Guo, Chenjuan Yang, Bin Hu, Jilin |
| author_facet | Liu, Xvyuan Qiu, Xiangfei Cheng, Hanyin Wu, Xingjian Guo, Chenjuan Yang, Bin Hu, Jilin |
| contents | Irregular multivariate time series (IMTS) are prevalent in critical domains like healthcare and finance, where accurate forecasting is vital for proactive decision-making. However, the asynchronous sampling and irregular intervals inherent to IMTS pose two core challenges for existing methods: (1) how to accurately represent the raw information of irregular time series without introducing data distortion, and (2) how to effectively capture the complex dynamic dependencies between observation points. To address these challenges, we propose the Adaptive Spatio-Temporal Graph Interaction (ASTGI) framework. Specifically, the framework first employs a Spatio-Temporal Point Representation module to encode each discrete observation as a point within a learnable spatio-temporal embedding space. Second, a Neighborhood-Adaptive Graph Construction module adaptively builds a causal graph for each point in the embedding space via nearest neighbor search. Subsequently, a Spatio-Temporal Dynamic Propagation module iteratively updates information on these adaptive causal graphs by generating messages and computing interaction weights based on the relative spatio-temporal positions between points. Finally, a Query Point-based Prediction module generates the final forecast by aggregating neighborhood information for a new query point and performing forecasting. Extensive experiments on multiple benchmark datasets demonstrate that ASTGI outperforms various state-of-the-art methods. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_23313 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting Liu, Xvyuan Qiu, Xiangfei Cheng, Hanyin Wu, Xingjian Guo, Chenjuan Yang, Bin Hu, Jilin Machine Learning Irregular multivariate time series (IMTS) are prevalent in critical domains like healthcare and finance, where accurate forecasting is vital for proactive decision-making. However, the asynchronous sampling and irregular intervals inherent to IMTS pose two core challenges for existing methods: (1) how to accurately represent the raw information of irregular time series without introducing data distortion, and (2) how to effectively capture the complex dynamic dependencies between observation points. To address these challenges, we propose the Adaptive Spatio-Temporal Graph Interaction (ASTGI) framework. Specifically, the framework first employs a Spatio-Temporal Point Representation module to encode each discrete observation as a point within a learnable spatio-temporal embedding space. Second, a Neighborhood-Adaptive Graph Construction module adaptively builds a causal graph for each point in the embedding space via nearest neighbor search. Subsequently, a Spatio-Temporal Dynamic Propagation module iteratively updates information on these adaptive causal graphs by generating messages and computing interaction weights based on the relative spatio-temporal positions between points. Finally, a Query Point-based Prediction module generates the final forecast by aggregating neighborhood information for a new query point and performing forecasting. Extensive experiments on multiple benchmark datasets demonstrate that ASTGI outperforms various state-of-the-art methods. |
| title | ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2509.23313 |