An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach
Current fuzzy collaborative forecasting methods have rarely considered how to determine the appropriate number of experts to optimize forecasting performance. Therefore, this study proposes an evolving partial-consensus fuzzy collaborative forecasting approach to address this issue. In the proposed...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/8/4/554 |
| _version_ | 1827718968917360640 |
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| author | Tin-Chih Toly Chen Yu-Cheng Wang Chin-Hau Huang |
| author_facet | Tin-Chih Toly Chen Yu-Cheng Wang Chin-Hau Huang |
| author_sort | Tin-Chih Toly Chen |
| collection | DOAJ |
| description | Current fuzzy collaborative forecasting methods have rarely considered how to determine the appropriate number of experts to optimize forecasting performance. Therefore, this study proposes an evolving partial-consensus fuzzy collaborative forecasting approach to address this issue. In the proposed approach, experts apply various fuzzy forecasting methods to forecast the same target, and the partial consensus fuzzy intersection operator, rather than the prevalent fuzzy intersection operator, is applied to aggregate the fuzzy forecasts by experts. Meaningful information can be determined by observing partial consensus fuzzy intersection changes as the number of experts varies, including the appropriate number of experts. We applied the evolving partial-consensus fuzzy collaborative forecasting approach to forecasting dynamic random access memory product yield with real data. The proposed approach forecasting performance surpassed current fuzzy collaborative forecasting that considered overall consensus, and it increased forecasting accuracy 13% in terms of mean absolute percentage error. |
| first_indexed | 2024-03-10T20:34:02Z |
| format | Article |
| id | doaj.art-8b317142a2a349b58ec49eed98fb01f2 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T20:34:02Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-8b317142a2a349b58ec49eed98fb01f22023-11-19T21:12:34ZengMDPI AGMathematics2227-73902020-04-018455410.3390/math8040554An Evolving Partial Consensus Fuzzy Collaborative Forecasting ApproachTin-Chih Toly Chen0Yu-Cheng Wang1Chin-Hau Huang2Department of Industrial Engineering and Management, National Chiao Tung University, 1001, University Road, Hsinchu 300, TaiwanDepartment of Aeronautical Engineering, Chaoyang University of Technology, Taichung 41349, TaiwanDepartment of Industrial Engineering and Management, National Chiao Tung University, 1001, University Road, Hsinchu 300, TaiwanCurrent fuzzy collaborative forecasting methods have rarely considered how to determine the appropriate number of experts to optimize forecasting performance. Therefore, this study proposes an evolving partial-consensus fuzzy collaborative forecasting approach to address this issue. In the proposed approach, experts apply various fuzzy forecasting methods to forecast the same target, and the partial consensus fuzzy intersection operator, rather than the prevalent fuzzy intersection operator, is applied to aggregate the fuzzy forecasts by experts. Meaningful information can be determined by observing partial consensus fuzzy intersection changes as the number of experts varies, including the appropriate number of experts. We applied the evolving partial-consensus fuzzy collaborative forecasting approach to forecasting dynamic random access memory product yield with real data. The proposed approach forecasting performance surpassed current fuzzy collaborative forecasting that considered overall consensus, and it increased forecasting accuracy 13% in terms of mean absolute percentage error.https://www.mdpi.com/2227-7390/8/4/554fuzzy collaborative forecastingdynamic random access memorypartial consensusfuzzy intersection |
| spellingShingle | Tin-Chih Toly Chen Yu-Cheng Wang Chin-Hau Huang An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach Mathematics fuzzy collaborative forecasting dynamic random access memory partial consensus fuzzy intersection |
| title | An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach |
| title_full | An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach |
| title_fullStr | An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach |
| title_full_unstemmed | An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach |
| title_short | An Evolving Partial Consensus Fuzzy Collaborative Forecasting Approach |
| title_sort | evolving partial consensus fuzzy collaborative forecasting approach |
| topic | fuzzy collaborative forecasting dynamic random access memory partial consensus fuzzy intersection |
| url | https://www.mdpi.com/2227-7390/8/4/554 |
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