A Novel Forecasting Method Based on F-Transform and Fuzzy Time Series
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Woo-Joo | - |
dc.contributor.author | Jung, Hye-Young | - |
dc.contributor.author | Yoon, Jin Hee | - |
dc.contributor.author | Choi, Seung Hoe | - |
dc.date.accessioned | 2021-06-22T15:22:56Z | - |
dc.date.available | 2021-06-22T15:22:56Z | - |
dc.date.created | 2021-01-22 | - |
dc.date.issued | 2017-07 | - |
dc.identifier.issn | 1562-2479 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/11601 | - |
dc.description.abstract | The main goal of time series analysis is to establish forecasting model based on past observations and to reduce forecasting error. To achieve these goals, the present paper proposes a new forecasting algorithm based on the fuzzy transform (F-transform) and the fuzzy logical relationships. First, the F-transform is performed based on partitioning of the universe, and the fuzzy logical relationships are employed to forecast. Two experimental applications are used to illustrate and verify the proposed algorithm. The accuracies are evaluated on the basis of average forecasting error percentage and index of agreement to compare the proposed algorithm with other existing methods. © 2017, The Author(s). | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer Berlin Heidelberg | - |
dc.title | A Novel Forecasting Method Based on F-Transform and Fuzzy Time Series | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jung, Hye-Young | - |
dc.identifier.doi | 10.1007/s40815-017-0354-6 | - |
dc.identifier.scopusid | 2-s2.0-85037334734 | - |
dc.identifier.wosid | 000417107100013 | - |
dc.identifier.bibliographicCitation | International Journal of Fuzzy Systems, v.19, no.6, pp.1793 - 1802 | - |
dc.relation.isPartOf | International Journal of Fuzzy Systems | - |
dc.citation.title | International Journal of Fuzzy Systems | - |
dc.citation.volume | 19 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 1793 | - |
dc.citation.endPage | 1802 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Automation & Control Systems | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Automation & Control Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.subject.keywordPlus | Fuzzy logic | - |
dc.subject.keywordPlus | Fuzzy systems | - |
dc.subject.keywordPlus | Time series | - |
dc.subject.keywordPlus | Time series analysis | - |
dc.subject.keywordPlus | Average forecasting error | - |
dc.subject.keywordPlus | Experimental application | - |
dc.subject.keywordPlus | Forecasting algorithm | - |
dc.subject.keywordPlus | Forecasting methods | - |
dc.subject.keywordPlus | Forecasting modeling | - |
dc.subject.keywordPlus | Fuzzy logical relationships | - |
dc.subject.keywordPlus | Fuzzy transforms | - |
dc.subject.keywordPlus | Index of agreements | - |
dc.subject.keywordPlus | Forecasting | - |
dc.subject.keywordAuthor | Forecasting | - |
dc.subject.keywordAuthor | Fuzzy logical relationship | - |
dc.subject.keywordAuthor | Fuzzy transform | - |
dc.subject.keywordAuthor | Time series | - |
dc.identifier.url | https://link.springer.com/article/10.1007/s40815-017-0354-6 | - |
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