Fuzzy Time Series Method for Forecasting Taiwan Export Data
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International Journal of Engineering Trends and Technology (IJETT) | 
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| © 2013 by IJETT Journal | ||
| Volume-4 Issue-8 |
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| Year of Publication : 2013 | ||
| Authors : P. Arumugam , V.Anithakumari |
Citation
P. Arumugam , V.Anithakumari. "Fuzzy Time Series Method for Forecasting Taiwan Export Data". International Journal of Engineering Trends and Technology (IJETT). V4(8):3342-3347 Jul 2013. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group.
Abstract
Forecasting accuracy is one of the most favorable critical issues in Autoregressive Integrated Moving A verage (ARIMA) models. The study compares the application of two forecasting methods on the amount of Taiwan export , the Fuzzy time series method and ARIMA method. Model discussed for the ARIMA method and Fuzzy time series method include the Sturges rules. When the sample period is extend in our models, the ARIMA models shows smaller than predicted error and closer predicted path to the realistic trend than those of the Fuzzy models, resulted in more accurate forecast of the export amount the Autoregressive Integrated Moving Average models. In the economic viewpoints, the amount of Taiwan export is mainly attributable to external factors. However, this impact reduces with time and export amount in the time series analysis . The ARIMA models can be utilized t o predicted export value accurately, when all of value or data is available
References
[1] Chi - chen wang, ” A comparison study between fuzzy time series model and ARIMA model for Forecasting Taiwan export , Expert system with Applica tion , Vol. 38, Pp. 9296 - 9304. 2011.
[2] Yun - sheng Hsu et.al “ A Comparison of ARIMA forecasting and heuristic modeling ” , Applied Financial Economics , Vol . 21, Pp. 1095 - 1102 ,2011 .
[3] Arumugam .P and Anithakumari . V , “ A comparison of Refined Ari thmetic and Fuzzy Time series Model for Taiwan Export Data, Proceeding of the International Conference on Mathematical Modeling and Applied Soft Computing, V olume - 2, Pp.210 - 220,2012
[4] AA bbasov A.M., Mamedova M.H. , Gasimov V.A. Fuzzy Relational Model for Knowledge Processing and Decision Making – Advance in Mathematics, New – York , 2002, vol.1, pp, 191 - 223 .
[5] Narendarakumar et.al, ” Fuzzy time series forecasting of Wheat production ” , International on Computer Science and Engineering , Issu l02, Vol No.03, Pp, 65 - 64 , 2010 .
[6] Shyi - ming Chen,”Forecasting enrollment Based on high order fuzzy time series,” Cybernetics and system , An International Journal , copyright, Taylor and Francis Vol, 33 , Pp , 1 - 16, 2002.
[7] Chung - ming own pao - Ta - Yu ,” Forecasting fuzzy time series on a heuristic high order mode ” l, Cybernetics and System , An International Journal , Taylor& Francis Inc. Vol, 36, Pp: 705 - 717, copyright 2005,
[8] Kunhuang Huarng .,” Heuristic model of fuzzy time series for forec asting ” , Fuzzy set and system , Elsever Vol: 123 , Pp. 369 - 386, 2001 .
Keywords
Time series; Stationary Stochastic Process ; Fuzzy Time series model ; ARIMA models; Sturges rule and Taiwan export.