A Novel First-Order Fuzzy Rules-Based Forecasting System Using Distance Measures Approach for Financial Market Forecasting
The precise estimates about finance, atmospheric science, power sector, industries, agriculture, and other science help governments and institutions economically in making the relevant policies regarding import-export, demand, consumption, storage, and local industries. Due to the uncertainty and no...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2023/8027664 |
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| author | Shahbaz Gul Hassan Tran Thi Kieuvan Shuangyin Liu Harish Garg Munawar Hassan Shafqat Iqbal |
| author_facet | Shahbaz Gul Hassan Tran Thi Kieuvan Shuangyin Liu Harish Garg Munawar Hassan Shafqat Iqbal |
| author_sort | Shahbaz Gul Hassan |
| collection | DOAJ |
| description | The precise estimates about finance, atmospheric science, power sector, industries, agriculture, and other science help governments and institutions economically in making the relevant policies regarding import-export, demand, consumption, storage, and local industries. Due to the uncertainty and nondeterministic behavior of data series with respect to time, the foremost challenge is to develop and identify the practical method to handle the above stated complex issues. As an illustration, this study presented an analysis of a new fuzzy time-series (FTS) approach and comparison with traditional forecasting models for prediction of gram pulse production. Taking into consideration the theory of fuzzy sets, FTS, fuzzy rules, triangular membership functions, distance measures, and modified weighted average method, a robust and effective fuzzy rules-based methodology was developed for the prediction of time-series data regarding crop production and share prices. Conventional statistical forecasting methods such as Holt’s linear trend, Holt’s exponential trend, and Holt’s damped exponential trend models were also applied on time-series data for comparison. To identify the primacy of modeling and forecasting, the techniques of root mean squared error (RMSE) and mean absolute error (MAE) were used as a criterion. The numerical values of RMSE and MAE such as 106.51 and 74.8897 clearly demonstrated that the proposed fuzzy rules-based method is robust for forecasting of production and market share prices in the environment of uncertainty. |
| format | Article |
| id | doaj-art-9152bcc53331482094f7f0d573f2a1fd |
| institution | Kabale University |
| issn | 2314-4785 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Mathematics |
| spelling | doaj-art-9152bcc53331482094f7f0d573f2a1fd2025-02-03T06:47:32ZengWileyJournal of Mathematics2314-47852023-01-01202310.1155/2023/8027664A Novel First-Order Fuzzy Rules-Based Forecasting System Using Distance Measures Approach for Financial Market ForecastingShahbaz Gul Hassan0Tran Thi Kieuvan1Shuangyin Liu2Harish Garg3Munawar Hassan4Shafqat Iqbal5College of Information Science and TechnologyZhongkai University of Agriculture and EngineeringCollege of Information Science and TechnologySchool of MathematicsSchool of Economics and ManagementSchool of Economics and StatisticsThe precise estimates about finance, atmospheric science, power sector, industries, agriculture, and other science help governments and institutions economically in making the relevant policies regarding import-export, demand, consumption, storage, and local industries. Due to the uncertainty and nondeterministic behavior of data series with respect to time, the foremost challenge is to develop and identify the practical method to handle the above stated complex issues. As an illustration, this study presented an analysis of a new fuzzy time-series (FTS) approach and comparison with traditional forecasting models for prediction of gram pulse production. Taking into consideration the theory of fuzzy sets, FTS, fuzzy rules, triangular membership functions, distance measures, and modified weighted average method, a robust and effective fuzzy rules-based methodology was developed for the prediction of time-series data regarding crop production and share prices. Conventional statistical forecasting methods such as Holt’s linear trend, Holt’s exponential trend, and Holt’s damped exponential trend models were also applied on time-series data for comparison. To identify the primacy of modeling and forecasting, the techniques of root mean squared error (RMSE) and mean absolute error (MAE) were used as a criterion. The numerical values of RMSE and MAE such as 106.51 and 74.8897 clearly demonstrated that the proposed fuzzy rules-based method is robust for forecasting of production and market share prices in the environment of uncertainty.http://dx.doi.org/10.1155/2023/8027664 |
| spellingShingle | Shahbaz Gul Hassan Tran Thi Kieuvan Shuangyin Liu Harish Garg Munawar Hassan Shafqat Iqbal A Novel First-Order Fuzzy Rules-Based Forecasting System Using Distance Measures Approach for Financial Market Forecasting Journal of Mathematics |
| title | A Novel First-Order Fuzzy Rules-Based Forecasting System Using Distance Measures Approach for Financial Market Forecasting |
| title_full | A Novel First-Order Fuzzy Rules-Based Forecasting System Using Distance Measures Approach for Financial Market Forecasting |
| title_fullStr | A Novel First-Order Fuzzy Rules-Based Forecasting System Using Distance Measures Approach for Financial Market Forecasting |
| title_full_unstemmed | A Novel First-Order Fuzzy Rules-Based Forecasting System Using Distance Measures Approach for Financial Market Forecasting |
| title_short | A Novel First-Order Fuzzy Rules-Based Forecasting System Using Distance Measures Approach for Financial Market Forecasting |
| title_sort | novel first order fuzzy rules based forecasting system using distance measures approach for financial market forecasting |
| url | http://dx.doi.org/10.1155/2023/8027664 |
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