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01-Applied Mathematics & Information Sciences
An International Journal
               
 
 
 
 
 
 
 
 
 
 
 
 

Content
 

Volumes > Volume 06 > No. 6-2S

 
   

Comparisons of Fuzzy Time Series and Hybrid Grey Model for Non-stationary Data Forecasting

PP: 409S-416S
Author(s)
Hsien-Lun Wong, Jau-Min Shiu,
Abstract
Fuzzy time series (FTS) and Grey model (GM) have been widely applied to forecasting problem in many fields. Rather than traditional econometric model, the two models have no prerequisites for time series normality or error calibration. In this paper, the prediction performance of FTS- heuristic model, two-factor model, Markov model and GM- GM(1,1), GM-Markov, GM-Fourier is investigated. The comparison of the models is based on forecasting error of time series. The high noise data, Taiwan export amount and foreign exchange rate obtained from AEROM, Taiwan are used for modelsí test. The results illustrate that the two forecasting models are appropriate for non-stationary time series. Among these models proposed, GM-Fourier residual modified model has best predictive performance. The study provides a beneficial reference of hybrid grey-based model in time series prediction.

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