Forecasting Winning Rates in Major League Baseball Based on Fuzzy Logic
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이우주 | - |
dc.contributor.author | 장효진 | - |
dc.contributor.author | 이서희 | - |
dc.contributor.author | 최승회 | - |
dc.date.accessioned | 2023-08-16T07:43:56Z | - |
dc.date.available | 2023-08-16T07:43:56Z | - |
dc.date.issued | 2020-10 | - |
dc.identifier.issn | 1976-9172 | - |
dc.identifier.issn | 2288-2324 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/114186 | - |
dc.description.abstract | A fuzzy time series is used efficiently when there are not enough data in a time series that is continuously recorded over time, or when data pattern cannot be expressed as a specific function. This study uses a fuzzy time series to predict the winning rates in Major League Baseball (MLB), using fuzzy partition and F-transform for the winning percentage of 16 teams in the MLB from 1901 to 2019. The results of the fuzzy time series presented in this study are compared with the results of the Box-Jenkins method and a Grey model using a differential equation for the increasing data, as well as the Long Short Term Memory (LSTM) model. To investigate the efficiency of the fuzzy time series analysis presented in this study, we use the mean absolute percentage error (MAPE), correlation, and ratio of deviation error. | - |
dc.description.abstract | 퍼지 시계열은 시계열의 자료 수가 충분하지 않거나 자료의 구조가 특정되지 않을 때에 효율적인 방법이다. 본 연구는 1901년부터 2019년까지 미국 메이저 리그에 소속된 16개 팀의 승률에 대한 종속성과상관성을 이용하여 승률에 대한 예측을 위하여 퍼지 논리와 퍼지 분할 그리고 F-변환을 이용한다. 그리고본 연구에서 제시된 퍼지 시계열의 결과를 전통적인 Arima 모형과 증가하는 자료에 대한 미분방정식을이용하는 Grey 방법, 그리고 장단기기억(Long Short Term Memory, LSTM) 방법의 결과와 비교한다.본 연구에서 제시된 퍼지 시계열 분석에 대한 효율성을 조사하기 위해 평균절대백분율오차와 편차와오차의 비 그리고 상관관계를 사용한다 | - |
dc.format.extent | 7 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | 한국지능시스템학회 | - |
dc.title | Forecasting Winning Rates in Major League Baseball Based on Fuzzy Logic | - |
dc.title.alternative | 퍼지논리를 이용한 야구 승률 예측 | - |
dc.type | Article | - |
dc.publisher.location | 대한민국 | - |
dc.identifier.doi | 10.5391/JKIIS.2020.30.5.366 | - |
dc.identifier.bibliographicCitation | 한국지능시스템학회 논문지, v.30, no.5, pp 366 - 372 | - |
dc.citation.title | 한국지능시스템학회 논문지 | - |
dc.citation.volume | 30 | - |
dc.citation.number | 5 | - |
dc.citation.startPage | 366 | - |
dc.citation.endPage | 372 | - |
dc.type.docType | 정기학술지(Article(Perspective Article포함)) | - |
dc.identifier.kciid | ART002638200 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | Fuzzy time series | - |
dc.subject.keywordAuthor | Winning rate | - |
dc.subject.keywordAuthor | F-transform | - |
dc.subject.keywordAuthor | Mean absolute percentage error | - |
dc.subject.keywordAuthor | 퍼지 시계열 | - |
dc.subject.keywordAuthor | 승률 | - |
dc.subject.keywordAuthor | F-변환 | - |
dc.subject.keywordAuthor | 평균절대백분율오차 | - |
dc.identifier.url | https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE10479040 | - |
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