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: Sep. 6, 2019
An Improved Fuzzy Time Series Forecasting Model Based on Combining K–means Clustering with Harmony Search
Pages: 94-102 Views: 178 Downloads: 103
Authors
[01] Nghiem Van Tinh, Faculty of Electronics, Thai Nguyen University of Technology, Thai Nguyen University, Thai Nguyen, Viet Nam.
Abstract
Various methods have been presented to investigate the length of data interval and partition number of universe of discourse in fuzzy time series (FTS) model to achieve high level forecasting accuracy. These resolve the fluctuations of FTS as mentioned by previous researchers. However, it still has difficulties in choosing the optimal length of interval. In this paper, we present a hybrid forecasting model based on combing K-mean clustering and Harmony search algorithm (HAS) to overcome the difficulties mentioned above. Firstly, the K-mean clustering algorithm is used to divide the historical data into clusters and adjust them into intervals with different initial length, then the strong global searching ability of particle swarm optimization is also used to adjust initial length and obtain the optimal length of interval universe of discourse with the aim to increase forecasting accuracy of model. Finally, two numerical datasets (enrollments data of the University of Alabama, and yearly deaths in car road accidents in Belgium) are used to verify the feasibility of the model by comparing and analyzing the forecasting accuracy between proposed model and other forecasting methods. The empirical analysis not only demonstrates the forecasting procedure and the way to obtain the suitable length of interval, but also shows that the proposed model significantly outperforms the conventional counterparts.
Keywords
Forecasting, Fuzzy Time Series, K–mean Clustering, Harmony Search, Enrolments
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