Comparative Study of Imputation Methods for Time Series Data
Missing and incomplete values pose a significant challenge in analyzing tabular and time-series data. Dealing with missing values is time-consuming and tedious, especially when working with data from real-world applications. While some imputation approaches estimate missing values based on existing...
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| Format: | Article |
| Language: | English |
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LibraryPress@UF
2023-05-01
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| Series: | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
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| Online Access: | https://journals.flvc.org/FLAIRS/article/view/133068 |
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| _version_ | 1849736531553550336 |
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| author | Daniyal Khan Alina Lazar |
| author_facet | Daniyal Khan Alina Lazar |
| author_sort | Daniyal Khan |
| collection | DOAJ |
| description | Missing and incomplete values pose a significant challenge in analyzing tabular and time-series data. Dealing with missing values is time-consuming and tedious, especially when working with data from real-world applications. While some imputation approaches estimate missing values based on existing observations, these methods often rely on strong assumptions about the data distribution, which only sometimes improves downstream accuracy. Although tabular imputation methods can be applied to time-series data, incorporating the time component can enhance accuracy. This study evaluates various techniques for missing data imputation in time-series data. We run experiments on four multi-variate time series datasets using five imputation methods. We report training time and testing accuracy. |
| format | Article |
| id | doaj-art-53d55e0a1e1249b0b0231cc2ad3dcb46 |
| institution | DOAJ |
| issn | 2334-0754 2334-0762 |
| language | English |
| publishDate | 2023-05-01 |
| publisher | LibraryPress@UF |
| record_format | Article |
| series | Proceedings of the International Florida Artificial Intelligence Research Society Conference |
| spelling | doaj-art-53d55e0a1e1249b0b0231cc2ad3dcb462025-08-20T03:07:14ZengLibraryPress@UFProceedings of the International Florida Artificial Intelligence Research Society Conference2334-07542334-07622023-05-013610.32473/flairs.36.13306869336Comparative Study of Imputation Methods for Time Series DataDaniyal Khan0Alina Lazar1Youngstown State UniversityYoungstown State UniversityMissing and incomplete values pose a significant challenge in analyzing tabular and time-series data. Dealing with missing values is time-consuming and tedious, especially when working with data from real-world applications. While some imputation approaches estimate missing values based on existing observations, these methods often rely on strong assumptions about the data distribution, which only sometimes improves downstream accuracy. Although tabular imputation methods can be applied to time-series data, incorporating the time component can enhance accuracy. This study evaluates various techniques for missing data imputation in time-series data. We run experiments on four multi-variate time series datasets using five imputation methods. We report training time and testing accuracy.https://journals.flvc.org/FLAIRS/article/view/133068time seriesrepresentation learninggraphgrincsdisaitspypotsmodeldiffusionsssdtabular datapartially-observed dataimputation |
| spellingShingle | Daniyal Khan Alina Lazar Comparative Study of Imputation Methods for Time Series Data Proceedings of the International Florida Artificial Intelligence Research Society Conference time series representation learning graph grin csdi saits pypots model diffusion sssd tabular data partially-observed data imputation |
| title | Comparative Study of Imputation Methods for Time Series Data |
| title_full | Comparative Study of Imputation Methods for Time Series Data |
| title_fullStr | Comparative Study of Imputation Methods for Time Series Data |
| title_full_unstemmed | Comparative Study of Imputation Methods for Time Series Data |
| title_short | Comparative Study of Imputation Methods for Time Series Data |
| title_sort | comparative study of imputation methods for time series data |
| topic | time series representation learning graph grin csdi saits pypots model diffusion sssd tabular data partially-observed data imputation |
| url | https://journals.flvc.org/FLAIRS/article/view/133068 |
| work_keys_str_mv | AT daniyalkhan comparativestudyofimputationmethodsfortimeseriesdata AT alinalazar comparativestudyofimputationmethodsfortimeseriesdata |