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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 145The Effects of Predictor Variables and Interval...

The Effects of Predictor Variables and Interval Partition on Fuzzy Time Series Forecasting

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Abstract:

Fuzzy time series methods have been applied to social forecasting for over a decade; however, little research has been done to discuss the decision of an optimal fuzzy model for time series. In the paper, we evaluate the forecasting performance of three listed multivariate fuzzy models by comparing forecasting MSE of model. The data obtained from AEROM, Taiwan, includes Taiwan’s exports and foreign exchange rate for models’ test. The algorithm for predictive value of the models has three-stage computation procedure: First, calibrating time series correlation, deciding window base and interval partition; second, solving the static forecasting value of each model; third, comparing the dynamic parameter to impact of the forecasting error. The empirical results indicate that increasing predictor variables has no significant effect on predictive performance of the models; increasing length of interval would not improve the prediction performance of the models. Moreover, Fuzzy model is better for short-term time series forecasting. For forecasting purpose, Heuristic model has best forecasting performance among three fuzzy models. The findings of the paper represent a significant contribution to our understanding of the applicability of fuzzy models to predict.

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Periodical:

Applied Mechanics and Materials (Volume 145)

Pages:

143-148

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.145.143

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Online since:

December 2011

Authors:

Hsien Lun Wong, Chi Chen Wang, Tsung Yi Shen

Keywords:

Fuzzy Time Series, Heuristic Model, Increment Information, Length of Interval, Markov Model, Two-Factor Model

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© 2012 Trans Tech Publications Ltd. All Rights Reserved

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