American Journal of Information Science and Computer Engineering
Articles Information
American Journal of Information Science and Computer Engineering, Vol.5, No.3, Sep. 2019, Pub. Date: Nov. 21, 2019
A Novel Forecasting Model Combining the High – Order Fuzzy Time Series with Particle Swam Optimization
Pages: 103-112 Views: 137 Downloads: 74
Authors
[01] Nghiem Van Tinh, Faculty of Electronics, Thai Nguyen University of Technology-Thai Nguyen University, Thai Nguyen, Viet Nam.
[02] Nguyen Tien Duy, Faculty of Electronics, Thai Nguyen University of Technology-Thai Nguyen University, Thai Nguyen, Viet Nam.
Abstract
This paper proposes a novel fuzzy time series forecasting model for contributing to the stages of determining of interval lengths, establishing of fuzzy logical relationships (FLRs) and the stage of defuzzification. In fuzzy time series (FTS) models, lengths of intervals always affect the results of forecasting. Therefore, we use particle swarm optimization (PSO) technique to find the optimal length of intervals in the universe of discourse. Most of the existing forecasting models simply ignore the repeated FLRs without any proper justification or accept the number of recurrence of the FLRs without considering the appearance history of these fuzzy sets in the grouping fuzzy logical relationships process. Therefore, in this study, we consider the appearance history of the fuzzy sets on the right-hand side of the FLRs to establish the high – order fuzzy logical relationship groups, called the high-order time-variant fuzzy relationship groups (TV-FRGs) and then, a new forecasting computational technique in the stage of defuzzification is introduced with the intend to obtain the smallest forecasting error as possible. For verifying the suitability of proposed method, two numerical datasets about enrollments of the University of Alabama and Gasonline Price in Viet Nam are illustrated for forecasting process and comparing with other forecasting model. Experimental results show that the proposed model outperforms other baseline forecasting model based on the high-order FTS.
Keywords
Forecasting, Fuzzy Time Series, Fuzzy Logical Relationships, PSO, Enrollments
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