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dc.contributor.authorNazirah, Ramli
dc.contributor.authorAdie Safian, Ton Mohamed
dc.contributor.authorNoor Izyan, Mohamad Adnan
dc.contributor.authorNik Muhammad Farhan Hakim, Nik Badrul Alam
dc.contributorSchool of Mathematics, Actuarial and Quantitative Studies, Asia Pacific University of Technology and Innovation, Malaysiaen_US
dc.contributorMathematical Sciences Studies, College of Computing, Informatics and Media, Universiti Teknologi MARA (UiTM) Pahang Branchen_US
dc.contributorSchool of Mathematics, Actuarial and Quantitative Studies, Asia Pacific University of Technology and Innovationen_US
dc.creatorNik Muhammad Farhan Hakim, Nik Badrul Alam
dc.date.accessioned2023-01-12T03:48:34Z
dc.date.available2023-01-12T03:48:34Z
dc.date.issued2022-12
dc.identifier.citationApplied Mathematics and Computational Intelligence (AMCI), vol.11(1), 2022, pages 325-335en_US
dc.identifier.issn2289-1315 (print)
dc.identifier.issn2289-1323 (online)
dc.identifier.urihttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/77658
dc.descriptionLink to publisher's homepage at https://amci.unimap.edu.my/en_US
dc.description.abstractForecasting time series data is crucial for predicting upcoming observations, especially in the market and business. Proper actions can be taken when there are some figures on future data, which are predicted based on the previous data. The fusion of fuzzy time series in forecasting has made forecasting using linguistic variables possible. However, the existence of extreme values in the time series data has led to inaccurate forecasting since the values are too large or too small. Hence, this paper proposes a hybrid fuzzy time series forecasting model with the 4253HT smoother to reduce the uncertainty of data. In this study, students’ enrolment data at the University of Alabama are implemented to illustrate the proposed hybrid forecasting model. The results show that the proposed model improves the forecasting performance since the mean square, root mean square, and mean absolute errors have been reduced. In the future, the implementation of data smoothing using the 4253HT smoother can be used in other fuzzy time series and intuitionistic fuzzy time series forecasting models.en_US
dc.language.isoenen_US
dc.publisherInstitute of Engineering Mathematics, Universiti Malaysia Perlisen_US
dc.subject.otherFuzzy time seriesen_US
dc.subject.other4253HT smootheren_US
dc.subject.otherStudents’ enrolmenten_US
dc.subject.otherTime series forecastingen_US
dc.titleA hybrid fuzzy time series forecasting model with 4253HT smootheren_US
dc.typeArticleen_US
dc.identifier.urlhttps://amci.unimap.edu.my/
dc.contributor.urlfarhanhakim@uitm.edu.myen_US


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