dc.contributor.author | Nazirah, Ramli | |
dc.contributor.author | Adie Safian, Ton Mohamed | |
dc.contributor.author | Noor Izyan, Mohamad Adnan | |
dc.contributor.author | Nik Muhammad Farhan Hakim, Nik Badrul Alam | |
dc.contributor | School of Mathematics, Actuarial and Quantitative Studies, Asia Pacific University of Technology and Innovation, Malaysia | en_US |
dc.contributor | Mathematical Sciences Studies, College of Computing, Informatics and Media, Universiti Teknologi MARA (UiTM) Pahang Branch | en_US |
dc.contributor | School of Mathematics, Actuarial and Quantitative Studies, Asia Pacific University of Technology and Innovation | en_US |
dc.creator | Nik Muhammad Farhan Hakim, Nik Badrul Alam | |
dc.date.accessioned | 2023-01-12T03:48:34Z | |
dc.date.available | 2023-01-12T03:48:34Z | |
dc.date.issued | 2022-12 | |
dc.identifier.citation | Applied Mathematics and Computational Intelligence (AMCI), vol.11(1), 2022, pages 325-335 | en_US |
dc.identifier.issn | 2289-1315 (print) | |
dc.identifier.issn | 2289-1323 (online) | |
dc.identifier.uri | http://dspace.unimap.edu.my:80/xmlui/handle/123456789/77658 | |
dc.description | Link to publisher's homepage at https://amci.unimap.edu.my/ | en_US |
dc.description.abstract | Forecasting 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.iso | en | en_US |
dc.publisher | Institute of Engineering Mathematics, Universiti Malaysia Perlis | en_US |
dc.subject.other | Fuzzy time series | en_US |
dc.subject.other | 4253HT smoother | en_US |
dc.subject.other | Students’ enrolment | en_US |
dc.subject.other | Time series forecasting | en_US |
dc.title | A hybrid fuzzy time series forecasting model with 4253HT smoother | en_US |
dc.type | Article | en_US |
dc.identifier.url | https://amci.unimap.edu.my/ | |
dc.contributor.url | farhanhakim@uitm.edu.my | en_US |