ImputeINR: Time Series Imputation via Implicit Neural Representations for Disease Diagnosis with Missing Data
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912379379384320 |
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| author | Li, Mengxuan Liu, Ke Guo, Jialong Bu, Jiajun Wang, Hongwei Wang, Haishuai |
| author_facet | Li, Mengxuan Liu, Ke Guo, Jialong Bu, Jiajun Wang, Hongwei Wang, Haishuai |
| contents | Healthcare data frequently contain a substantial proportion of missing values, necessitating effective time series imputation to support downstream disease diagnosis tasks. However, existing imputation methods focus on discrete data points and are unable to effectively model sparse data, resulting in particularly poor performance for imputing substantial missing values. In this paper, we propose a novel approach, ImputeINR, for time series imputation by employing implicit neural representations (INR) to learn continuous functions for time series. ImputeINR leverages the merits of INR in that the continuous functions are not coupled to sampling frequency and have infinite sampling frequency, allowing ImputeINR to generate fine-grained imputations even on extremely sparse observed values. Extensive experiments conducted on eight datasets with five ratios of masked values show the superior imputation performance of ImputeINR, especially for high missing ratios in time series data. Furthermore, we validate that applying ImputeINR to impute missing values in healthcare data enhances the performance of downstream disease diagnosis tasks. Codes are available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10856 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ImputeINR: Time Series Imputation via Implicit Neural Representations for Disease Diagnosis with Missing Data Li, Mengxuan Liu, Ke Guo, Jialong Bu, Jiajun Wang, Hongwei Wang, Haishuai Machine Learning Artificial Intelligence Healthcare data frequently contain a substantial proportion of missing values, necessitating effective time series imputation to support downstream disease diagnosis tasks. However, existing imputation methods focus on discrete data points and are unable to effectively model sparse data, resulting in particularly poor performance for imputing substantial missing values. In this paper, we propose a novel approach, ImputeINR, for time series imputation by employing implicit neural representations (INR) to learn continuous functions for time series. ImputeINR leverages the merits of INR in that the continuous functions are not coupled to sampling frequency and have infinite sampling frequency, allowing ImputeINR to generate fine-grained imputations even on extremely sparse observed values. Extensive experiments conducted on eight datasets with five ratios of masked values show the superior imputation performance of ImputeINR, especially for high missing ratios in time series data. Furthermore, we validate that applying ImputeINR to impute missing values in healthcare data enhances the performance of downstream disease diagnosis tasks. Codes are available. |
| title | ImputeINR: Time Series Imputation via Implicit Neural Representations for Disease Diagnosis with Missing Data |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2505.10856 |