An Evaluation of Comparison between Multivariate Fuzzy Time Series with Traditional Time Series Model for Forecasting Taiwan Export
Los Angeles, CA
March 31, 2009 to April 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.827
Fuzzy Time Series methods have been applied for forecasting problems for over one decade. In this paper, we evaluate the forecasting accuracy by comparing three popular multivariate Fuzzy Time Series models (MFTS) with two Traditional Time Series models. The real world case of Taiwan exports is employed for models’ test to compare the forecasting ability among models and to examine the effects of different lengths of interval and increment information on the forecasting error of models. The data used for model’s test includes two factors obtained from AREMOS, Taiwan. The results illustrates that MFTS are more appropriate for a short term prediction than ARIMA. Introducing increment information is not necessarily in improving the forecasting ability of fuzzy time series. Moreover, Heuristic method has the lowest MSE in MFTS.
Hsien-Lun Wong, Yi-Hsien Tu, Chi-Chen Wang, "An Evaluation of Comparison between Multivariate Fuzzy Time Series with Traditional Time Series Model for Forecasting Taiwan Export", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 462-467, doi:10.1109/CSIE.2009.827