A Novel First-Order Fuzzy Rules-Based Forecasting System Using Distance Measures Approach for Financial Market Forecasting

Author:

Hassan Shahbaz Gul1ORCID,Kieuvan Tran Thi2,Liu Shuangyin1ORCID,Garg Harish3456ORCID,Hassan Munawar7ORCID,Iqbal Shafqat8ORCID

Affiliation:

1. College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China

2. Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China

3. School of Mathematics, Thapar Institute of Engineering and Technology (Deemed University), Patiala 147004, Punjab, India

4. Department of Mathematics, Graphic Era Deemed to be University, Dehradun 248002, Uttarakhand, India

5. Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan

6. College of Technical Engineering, The Islamic University, Najaf, Iraq

7. School of Economics and Management, Jilin Agriculture University, Changchun 130033, China

8. School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China

Abstract

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.

Funder

National Natural Science Foundation of China

Publisher

Hindawi Limited

Subject

General Mathematics

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