Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/42940
Title: A heuristic time-invariant model for fuzzy time series forecasting
Authors: Bai, EJ
Wong, WK
Chu, WC
Xia, M
Pan, F
Keywords: Fuzzy time series
Heuristic model
Forecasting
Enrollment
TAIFEX
Issue Date: 2011
Publisher: Pergamon Press
Source: Expert systems with applications, 2011, v. 38, no. 3, p. 2701-2707 How to cite?
Journal: Expert systems with applications 
Abstract: Many forecasting models based on the concepts of fuzzy time series have been proposed in the past decades. These models have been applied to predict enrollments, temperature, crop production and stock index, etc. In this paper, we present a simple heuristic time-invariant fuzzy time series forecasting model, which uses prediction accuracy of model observations to train the trend predictor in the training phase, and uses these trend predictor to generate forecasting values in the testing phase. This model can capture the trends of the time series more accurately and hence improve the forecasting results. The proposed method is applied for forecasting university enrollment of Alabama and the Taiwan Futures Exchange (TAIFEX). It is shown that the proposed model achieves a significant improvement in forecasting accuracy as compared to other fuzzy time series forecasting models.
URI: http://hdl.handle.net/10397/42940
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2010.08.059
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