The optimal interval partition and second-factor fuzzy set bi on the impacts of fuzzy time series forecasting

  • Authors:

  • Chi-Chen Wang;Yueh-Ju Lin;Yu-Ren Zhang;Hsien-Lun Wong

  • Affiliations:

  • Department of Financial Management, National Defense University, Taipei, Taiwan;Department of Accounting, Kainan University, Taoyuan, Taiwan;Department of Accounting, Kainan University, Taoyuan, Taiwan;Institute of Management, MingHsin University of Science and Technology, HsinFong, HsinChu, Taiwan

  • Venue:
  • WSEAS Transactions on Circuits and Systems
  • Year:
  • 2011

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Abstract

This study uses two sets of Taiwanese data, the export values as the prediction varialbe and its foreign exchange spot rates as the auxiliary variable, to discuss two important issues of forecasting effects in the fuzzy time series analysis by using One- and Two-factor models. The first issue is the relation between the optimum number of partition equal intervals and forecasting error. The second issue is the setting of fuzzy matrix (Bi) in the model to compare its impacts on forecasting error when it is static or dynamic. The above two issues are investigated with the empirical results. First, the optimum number of partition equal intervals is to select 14 intervals for the information to have the smallest forecasting error in all models for one- or twofactor, or different number of window basis selected. However, if partitioning the information into more than 14 equal intervals, the forecasting error can not be reduced but presents a waving pattern. Second, when the information period is longer and if the selecting window basis is two, under any number of partition intervals, the forecasting error is always smaller for the dynamic Bi than for the static one. However, when the information period is shorter and the window basis is two or three, only partitioning into five or eight equal intervals, the forecasting error will also be smaller for the dynamic Bi.