Fuzzy Time Series Forecasting Model Based on Automatic Clustering Techniques and Generalized Fuzzy Logical Relationship

Wangren Qiu,Ping Zhang,Yanhong Wang

doi:10.1155/2015/962597

Abstract

In view of techniques for constructing high-order fuzzy time series models, there are three types which are based on advanced algorithms, computational method, and grouping the fuzzy logical relationships. The last type of models is easy to be understood by the decision maker who does not know anything about fuzzy set theory or advanced algorithms. To deal with forecasting problems, this paper presented novel high-order fuzz time series models denoted as GTS(M, N)based on generalized fuzzy logical relationships and automatic clustering. This paper issued the concept of generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the procedure of the proposed model was implemented on forecasting enrollment data at the University of Alabama. To show the considerable outperforming results, the proposed approach was also applied to forecasting the Shanghai Stock Exchange Composite Index. Finally, the effects of parametersMandN, the number of order, and concerned principal fuzzy logical relationships, on the forecasting results were also discussed.

Highlights

In nearly two decades, fuzzy time series approach introduced by Song and Chissom has been used widely for its superiorities in dealing with imprecise knowledge variables in decision making
Lee et al [11,12,13,14] presented several fuzzy forecast models based on the fuzzy time series, genetic algorithm, the simulated annealing algorithm, and type-2 fuzzy set to forecast temperature and TAIFEX
Since the proposed method is a fuzzy time series model related to the number of factors denoted as M and principal fuzzy relationship denoted as N, we name it GTS(M, N)

Summary

Introduction

Fuzzy time series approach introduced by Song and Chissom has been used widely for its superiorities in dealing with imprecise knowledge (like linguistic) variables in decision making. For these reasons, Chen et al [19,20,21,22,23] proposed some new methods which analyze high-order fuzzy time series forecasting model to deal with the enrollments forecasting problem. The forecasting procedure and principles had been expressed clearly for fuzzy time series researchers and easy to be understood by the decision maker who does not know anything about fuzzy set theory or prerequisite advanced algorithms. For these reasons, this study proposes a high-order fuzzy time series model based on automatic clustering [28,29,30] and generalized fuzzy logical relationships [31]. In view of the three criteria of evaluations: the root mean squared error, mean absolute error, and mean absolute percentage error, the proposed method gets a higher forecasting accuracy rate than the counterparts

Preliminaries

Proposed Model

Empirical Analysis

Conclusion

Conflict of Interests