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Improved models in fuzzy time series for forecasting

Sadaei, Hossein Javedani (2013) Improved models in fuzzy time series for forecasting. PhD thesis, Universiti Teknologi Malaysia, Faculty of Science.



The focus of this research is in the area of fuzzy time series. Such a study is important in order to improve the forecasting performance. The research approach adopted in this thesis includes introducing polynomial fuzzy time series, di�erential fuzzy logic relationships model, multi-layer stock forecasting model, data pre-processing approach, and k-step-ahead forecasting. The �ndings from this research provide evidence that integration of the polynomial concept and non- linear optimization transfer the fuzzy time series to a parametric model. By using polynomial fuzzy time series, 83% of experiments were improved signi�cantly. Di�erential fuzzy logical relationships were de�ned to be used for establishing di�erential fuzzy logical relationship groups. By utilizing di�erential fuzzy time series in Taiwan Capitalization Weighted Stock Index (TAIEX) datasets, 90% of the results were improved and as for enrollment datasets this statistic was 100%. Data pre-processing approach managed to reduce the negative e�ects of noisy data by transforming the data into a new domain. By applying integrated data pre-processing fuzzy time series algorithm to short term load data and TAIEX, the average of Mean Absolute Percentage Errors (MAPEs) and Root Mean Square Errors (RMSEs) were reduced by 12.05 and 1.98, respectively. The multi-layer forecasting model enhances the performance of stock forecast values. Many experiments that were carried out on the forty years' stock data indicated that multi-layer fuzzy time series model could be considered as an advanced model for stock market forecasting. The one-day ahead forecasting was successfully employed to England and France 2006 half-hourly load data. The main conclusion drawn from this study suggests that the proposed methods were accurate compared to their counterparts. In addition, the functionality of the proposed methods was enhanced through the proposed algorithms which were tested to be robust and reliable. All of these �ndings were con�rmed through various tests of the proposed methods on numerous case studies. The thesis also recommends that the fuzzy time series model should be considered in forecasting alongside with classical approaches.

Item Type:Thesis (PhD)
Additional Information:Thesis (Ph.D (Matematik)) - Universiti Teknologi Malaysia, 2013; Supervisors : Prof. Dr. Muhammad Hisyam Lee, Dr. Suhartono
Uncontrolled Keywords:time-series analysis, forecasting--statistical methods, polynomials
Subjects:Q Science > QA Mathematics
ID Code:34630
Deposited By: Kamariah Mohamed Jong
Deposited On:19 Feb 2014 09:15
Last Modified:17 Jul 2017 15:31

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