Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network
(1) Background: In order to solve the problem of missing time-series data due to the influence of the acquisition system or external factors, a missing time-series data interpolation method based on random forest and a generative adversarial interpolation network is proposed. (2) Methods: First, the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/4/1112 |
| _version_ | 1827342821900681216 |
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| author | Hongsen Ou Yunan Yao Yi He |
| author_facet | Hongsen Ou Yunan Yao Yi He |
| author_sort | Hongsen Ou |
| collection | DOAJ |
| description | (1) Background: In order to solve the problem of missing time-series data due to the influence of the acquisition system or external factors, a missing time-series data interpolation method based on random forest and a generative adversarial interpolation network is proposed. (2) Methods: First, the position of the missing part of the data is calibrated, and the trained random forest algorithm is used for the first data interpolation. The output value of the random forest algorithm is used as the input value of the generative adversarial interpolation network, and the generative adversarial interpolation network is used to calibrate the position. The data are interpolated for the second time, and the advantages of the two algorithms are combined to make the interpolation result closer to the true value. (3) Results: The filling effect of the algorithm is tested on a certain bearing data set, and the root mean square error (RMSE) is used to evaluate the interpolation results. The results show that the RMSE of the interpolation results based on the random forest and generative adversarial interpolation network algorithms in the case of single-segment and multi-segment missing data is only 0.0157, 0.0386, and 0.0527, which is better than the random forest algorithm, generative adversarial interpolation network algorithm, and K-nearest neighbor algorithm. (4) Conclusions: The proposed algorithm performs well in each data set and provides a reference method in the field of data filling. |
| first_indexed | 2024-03-07T22:15:03Z |
| format | Article |
| id | doaj.art-86f1123f8b3040b88a6c2dee86884f9f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-07T22:15:03Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-86f1123f8b3040b88a6c2dee86884f9f2024-02-23T15:33:36ZengMDPI AGSensors1424-82202024-02-01244111210.3390/s24041112Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation NetworkHongsen Ou0Yunan Yao1Yi He2School of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, ChinaSchool of Naval Architecture, Ocean and Energy Power Engineering, Wuhan University of Technology, Wuhan 430063, China(1) Background: In order to solve the problem of missing time-series data due to the influence of the acquisition system or external factors, a missing time-series data interpolation method based on random forest and a generative adversarial interpolation network is proposed. (2) Methods: First, the position of the missing part of the data is calibrated, and the trained random forest algorithm is used for the first data interpolation. The output value of the random forest algorithm is used as the input value of the generative adversarial interpolation network, and the generative adversarial interpolation network is used to calibrate the position. The data are interpolated for the second time, and the advantages of the two algorithms are combined to make the interpolation result closer to the true value. (3) Results: The filling effect of the algorithm is tested on a certain bearing data set, and the root mean square error (RMSE) is used to evaluate the interpolation results. The results show that the RMSE of the interpolation results based on the random forest and generative adversarial interpolation network algorithms in the case of single-segment and multi-segment missing data is only 0.0157, 0.0386, and 0.0527, which is better than the random forest algorithm, generative adversarial interpolation network algorithm, and K-nearest neighbor algorithm. (4) Conclusions: The proposed algorithm performs well in each data set and provides a reference method in the field of data filling.https://www.mdpi.com/1424-8220/24/4/1112random forestgenerative adversarial interpolation networktime-series datadata interpolation |
| spellingShingle | Hongsen Ou Yunan Yao Yi He Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network Sensors random forest generative adversarial interpolation network time-series data data interpolation |
| title | Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network |
| title_full | Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network |
| title_fullStr | Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network |
| title_full_unstemmed | Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network |
| title_short | Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network |
| title_sort | missing data imputation method combining random forest and generative adversarial imputation network |
| topic | random forest generative adversarial interpolation network time-series data data interpolation |
| url | https://www.mdpi.com/1424-8220/24/4/1112 |
| work_keys_str_mv | AT hongsenou missingdataimputationmethodcombiningrandomforestandgenerativeadversarialimputationnetwork AT yunanyao missingdataimputationmethodcombiningrandomforestandgenerativeadversarialimputationnetwork AT yihe missingdataimputationmethodcombiningrandomforestandgenerativeadversarialimputationnetwork |