Improved models in fuzzy time series for forecasting - Sadaei, Hossein Javedani (2013)
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, dierential 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 signicantly. Dierential fuzzy logical relationships were dened to be used for establishing dierential fuzzy logical relationship groups. By utilizing dierential 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 eects 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 conrmed 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.