Time-series imputation using low-rank matrix completion
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913458712215552 |
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| author | Poudevigne, Thomas Jones, Owen |
| author_facet | Poudevigne, Thomas Jones, Owen |
| contents | We investigate the use of matrix completion methods for time-series imputation. Specifically we consider low-rank completion of the block-Hankel matrix representation of a time-series. Simulation experiments are used to compare the method with five recognised imputation techniques with varying levels of computational effort. The Hankel Imputation (HI) method is seen to perform competitively at interpolating missing time-series data, and shows particular potential for reproducing sharp peaks in the data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02594 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Time-series imputation using low-rank matrix completion Poudevigne, Thomas Jones, Owen Methodology 62M10 We investigate the use of matrix completion methods for time-series imputation. Specifically we consider low-rank completion of the block-Hankel matrix representation of a time-series. Simulation experiments are used to compare the method with five recognised imputation techniques with varying levels of computational effort. The Hankel Imputation (HI) method is seen to perform competitively at interpolating missing time-series data, and shows particular potential for reproducing sharp peaks in the data. |
| title | Time-series imputation using low-rank matrix completion |
| topic | Methodology 62M10 |
| url | https://arxiv.org/abs/2408.02594 |