Investigating a Model-Agnostic and Imputation-Free Approach for Irregularly-Sampled Multivariate Time-Series Modeling
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911366829309952 |
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| author | Neog, Abhilash Daw, Arka Khorasgani, Sepideh Fatemi Sawhney, Medha Pradhan, Aanish Lofton, Mary E. McAfee, Bennett J. Breef-Pilz, Adrienne Wander, Heather L. Howard, Dexter W Carey, Cayelan C. Hanson, Paul Karpatne, Anuj |
| author_facet | Neog, Abhilash Daw, Arka Khorasgani, Sepideh Fatemi Sawhney, Medha Pradhan, Aanish Lofton, Mary E. McAfee, Bennett J. Breef-Pilz, Adrienne Wander, Heather L. Howard, Dexter W Carey, Cayelan C. Hanson, Paul Karpatne, Anuj |
| contents | Modeling Irregularly-sampled and Multivariate Time Series (IMTS) is crucial across a variety of applications where different sets of variates may be missing at different time-steps due to sensor malfunctions or high data acquisition costs. Existing approaches for IMTS either consider a two-stage impute-then-model framework or involve specialized architectures specific to a particular model and task. We perform a series of experiments to derive novel insights about the performance of IMTS methods on a variety of semi-synthetic and real-world datasets for both classification and forecasting. We also introduce Missing Feature-aware Time Series Modeling (MissTSM) or MissTSM, a novel model-agnostic and imputation-free approach for IMTS modeling. We show that MissTSM shows competitive performance compared to other IMTS approaches, especially when the amount of missing values is large and the data lacks simplistic periodic structures - conditions common to real-world IMTS applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_15785 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Investigating a Model-Agnostic and Imputation-Free Approach for Irregularly-Sampled Multivariate Time-Series Modeling Neog, Abhilash Daw, Arka Khorasgani, Sepideh Fatemi Sawhney, Medha Pradhan, Aanish Lofton, Mary E. McAfee, Bennett J. Breef-Pilz, Adrienne Wander, Heather L. Howard, Dexter W Carey, Cayelan C. Hanson, Paul Karpatne, Anuj Machine Learning Artificial Intelligence Modeling Irregularly-sampled and Multivariate Time Series (IMTS) is crucial across a variety of applications where different sets of variates may be missing at different time-steps due to sensor malfunctions or high data acquisition costs. Existing approaches for IMTS either consider a two-stage impute-then-model framework or involve specialized architectures specific to a particular model and task. We perform a series of experiments to derive novel insights about the performance of IMTS methods on a variety of semi-synthetic and real-world datasets for both classification and forecasting. We also introduce Missing Feature-aware Time Series Modeling (MissTSM) or MissTSM, a novel model-agnostic and imputation-free approach for IMTS modeling. We show that MissTSM shows competitive performance compared to other IMTS approaches, especially when the amount of missing values is large and the data lacks simplistic periodic structures - conditions common to real-world IMTS applications. |
| title | Investigating a Model-Agnostic and Imputation-Free Approach for Irregularly-Sampled Multivariate Time-Series Modeling |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.15785 |