Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913548791185408 |
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| author | Zheng, Liangwei Nathan Li, Zhengyang Dong, Chang George Zhang, Wei Emma Yue, Lin Xu, Miao Maennel, Olaf Chen, Weitong |
| author_facet | Zheng, Liangwei Nathan Li, Zhengyang Dong, Chang George Zhang, Wei Emma Yue, Lin Xu, Miao Maennel, Olaf Chen, Weitong |
| contents | Irregular Time Series Data (IRTS) has shown increasing prevalence in real-world applications. We observed that IRTS can be divided into two specialized types: Natural Irregular Time Series (NIRTS) and Accidental Irregular Time Series (AIRTS). Various existing methods either ignore the impacts of irregular patterns or statically learn the irregular dynamics of NIRTS and AIRTS data and suffer from limited data availability due to the sparsity of IRTS. We proposed a novel transformer-based framework for general irregular time series data that treats IRTS from four views: Locality, Time, Spatio and Irregularity to motivate the data usage to the highest potential. Moreover, we design a sophisticated irregularity-gate mechanism to adaptively select task-relevant information from irregularity, which improves the generalization ability to various IRTS data. We implement extensive experiments to demonstrate the resistance of our work to three highly missing ratio datasets (88.4\%, 94.9\%, 60\% missing value) and investigate the significance of the irregularity information for both NIRTS and AIRTS by additional ablation study. We release our implementation in https://github.com/IcurasLW/MTSFormer-Irregular_Time_Series.git |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_12257 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics Zheng, Liangwei Nathan Li, Zhengyang Dong, Chang George Zhang, Wei Emma Yue, Lin Xu, Miao Maennel, Olaf Chen, Weitong Machine Learning Irregular Time Series Data (IRTS) has shown increasing prevalence in real-world applications. We observed that IRTS can be divided into two specialized types: Natural Irregular Time Series (NIRTS) and Accidental Irregular Time Series (AIRTS). Various existing methods either ignore the impacts of irregular patterns or statically learn the irregular dynamics of NIRTS and AIRTS data and suffer from limited data availability due to the sparsity of IRTS. We proposed a novel transformer-based framework for general irregular time series data that treats IRTS from four views: Locality, Time, Spatio and Irregularity to motivate the data usage to the highest potential. Moreover, we design a sophisticated irregularity-gate mechanism to adaptively select task-relevant information from irregularity, which improves the generalization ability to various IRTS data. We implement extensive experiments to demonstrate the resistance of our work to three highly missing ratio datasets (88.4\%, 94.9\%, 60\% missing value) and investigate the significance of the irregularity information for both NIRTS and AIRTS by additional ablation study. We release our implementation in https://github.com/IcurasLW/MTSFormer-Irregular_Time_Series.git |
| title | Irregularity-Informed Time Series Analysis: Adaptive Modelling of Spatial and Temporal Dynamics |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.12257 |