THE APPLICATION OF HEDGE ALGEBRAS IN FUZZY TIME SERIES FORECASTING

Nguyễn Cát Hồ, Nguyễn Công Điều, Vũ Như Lân

Abstract


Fuzzy time series given by Song & Chissom (1993) in magazine "Fuzzy Sets and   Systems" has been widely studied for forecasting purposes. However, the accuracy of forecasts based on the concept of fuzzy approach of Song & Chissom is not high because of such depends on many factors. Chen (1996) proposed an efficient fuzzy time series model which consists of simple arithmetic calculations only. After that, this has been widely studied for improving accuracy of forecasting in many applications to get better results.

The hedge algebras developed by Nguyen and Wechler (1990) was completely different from the fuzzy approach. Here the hedge algebras was used to model  linguistic domains and variables and their semantic structure is obtained. Instead of performing fuzzification and defuzzification, more simple methods are adopted, termed as semantization and desemantization, respectively. The hedge algebras based fuzzy system is a new topic, which was first applied to fuzzy control 2008 [16]. Hedge algebras applications for some specific problems in the field of information technology and control has a number of important results and confirm advantages of this approach in comparing with fuzzy approach. In continuilty of hedge algebras applications, this paper is mainly focused on the field of  fuzzy time series forecasting under hedge algebras approach.

In this paper, we present a new approach using hedge algebras to provide a computational model, which is completely different from the fuzzy approach for fuzzy time series forecasting. The experimental results of forecasting enrollments of students of the University of Alabama show that the model of fuzzy time series based on hedge algebras is better than many existing models. We can see that the proposed model gains higher forecasting accuracy than the original model presented by Song and Chissom (1993b), Chen (1996, 2002), or Lee (2009), Qiu (2011), Egrioglu (2012), Ozdemir ( 2012) and Uslu (2013).


Keywords


fuzzy time series, prediction model



DOI: https://doi.org/10.15625/0866-708X/54/2/5242 Display counter: Abstract : 83 views. PDF (Tiếng Việt) : 223 views.

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Published by Vietnam Academy of Science and Technology