Improving the Accuracy of Forecasting Models Using the Modified Model of Single-Valued Neutrosophic Hesitant Fuzzy Time Series
Proposed in this study is a modified model for a single-valued neutrosophic hesitant fuzzy time series forecasting of the time series data. The research aims at improving the previously presented single-valued neutrosophic hesitant fuzzy time series (SVNHFTS) model by including several degrees of he...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Axioms |
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| Online Access: | https://www.mdpi.com/2075-1680/11/10/527 |
| _version_ | 1797475338642522112 |
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| author | Kittikun Pantachang Roengchai Tansuchat Woraphon Yamaka |
| author_facet | Kittikun Pantachang Roengchai Tansuchat Woraphon Yamaka |
| author_sort | Kittikun Pantachang |
| collection | DOAJ |
| description | Proposed in this study is a modified model for a single-valued neutrosophic hesitant fuzzy time series forecasting of the time series data. The research aims at improving the previously presented single-valued neutrosophic hesitant fuzzy time series (SVNHFTS) model by including several degrees of hesitancy to increase forecasting accuracy. The Gaussian fuzzy number (GFN) and the bell-shaped fuzzy number (BSFN) were used to incorporate the degree of hesitancy. The cosine measure and the single-valued neutrosophic hesitant fuzzy weighted geometric (SVNHFWG) operator were applied to analyze the possibilities and pick the best one based on the neutrosophic value. Two data sets consist of the short and low-frequency time series data of student enrollment and the long and high-frequency data of ten major cryptocurrencies. The empirical result demonstrated that the proposed model provides higher efficiency and accuracy in forecasting the daily closing prices of ten major cryptocurrencies compared to the S-ANFIS, ARIMA, and LSTM methods and also outperforms other FTS methods in predicting the benchmark student enrollment dataset of the University of Alabama in terms of computation time, the Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE). |
| first_indexed | 2024-03-09T20:42:46Z |
| format | Article |
| id | doaj.art-677a38cfd45f495f96defc0f144a0607 |
| institution | Directory Open Access Journal |
| issn | 2075-1680 |
| language | English |
| last_indexed | 2024-03-09T20:42:46Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj.art-677a38cfd45f495f96defc0f144a06072023-11-23T22:53:49ZengMDPI AGAxioms2075-16802022-10-01111052710.3390/axioms11100527Improving the Accuracy of Forecasting Models Using the Modified Model of Single-Valued Neutrosophic Hesitant Fuzzy Time SeriesKittikun Pantachang0Roengchai Tansuchat1Woraphon Yamaka2Faculty of Economics, Chiang Mai University, Chiang Mai 50200, ThailandCenter of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Chiang Mai 50200, ThailandCenter of Excellence in Econometrics, Faculty of Economics, Chiang Mai University, Chiang Mai 50200, ThailandProposed in this study is a modified model for a single-valued neutrosophic hesitant fuzzy time series forecasting of the time series data. The research aims at improving the previously presented single-valued neutrosophic hesitant fuzzy time series (SVNHFTS) model by including several degrees of hesitancy to increase forecasting accuracy. The Gaussian fuzzy number (GFN) and the bell-shaped fuzzy number (BSFN) were used to incorporate the degree of hesitancy. The cosine measure and the single-valued neutrosophic hesitant fuzzy weighted geometric (SVNHFWG) operator were applied to analyze the possibilities and pick the best one based on the neutrosophic value. Two data sets consist of the short and low-frequency time series data of student enrollment and the long and high-frequency data of ten major cryptocurrencies. The empirical result demonstrated that the proposed model provides higher efficiency and accuracy in forecasting the daily closing prices of ten major cryptocurrencies compared to the S-ANFIS, ARIMA, and LSTM methods and also outperforms other FTS methods in predicting the benchmark student enrollment dataset of the University of Alabama in terms of computation time, the Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and the Root Mean Square Error (RMSE).https://www.mdpi.com/2075-1680/11/10/527cryptocurrency forecastingstudent enrollmentfuzzy time seriessingle-valued neuromorphic hesitant fuzzy time series |
| spellingShingle | Kittikun Pantachang Roengchai Tansuchat Woraphon Yamaka Improving the Accuracy of Forecasting Models Using the Modified Model of Single-Valued Neutrosophic Hesitant Fuzzy Time Series Axioms cryptocurrency forecasting student enrollment fuzzy time series single-valued neuromorphic hesitant fuzzy time series |
| title | Improving the Accuracy of Forecasting Models Using the Modified Model of Single-Valued Neutrosophic Hesitant Fuzzy Time Series |
| title_full | Improving the Accuracy of Forecasting Models Using the Modified Model of Single-Valued Neutrosophic Hesitant Fuzzy Time Series |
| title_fullStr | Improving the Accuracy of Forecasting Models Using the Modified Model of Single-Valued Neutrosophic Hesitant Fuzzy Time Series |
| title_full_unstemmed | Improving the Accuracy of Forecasting Models Using the Modified Model of Single-Valued Neutrosophic Hesitant Fuzzy Time Series |
| title_short | Improving the Accuracy of Forecasting Models Using the Modified Model of Single-Valued Neutrosophic Hesitant Fuzzy Time Series |
| title_sort | improving the accuracy of forecasting models using the modified model of single valued neutrosophic hesitant fuzzy time series |
| topic | cryptocurrency forecasting student enrollment fuzzy time series single-valued neuromorphic hesitant fuzzy time series |
| url | https://www.mdpi.com/2075-1680/11/10/527 |
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