High-Order Fuzzy Time Series Model Based on Generalized Fuzzy Logical Relationship

Wangren Qiu,Hailin Li,Xiaodong Liu

doi:10.1155/2013/927394

Abstract

In view of techniques for constructing high-order fuzzy time series models, there are three methods which are based on advanced algorithms, computational methods, and grouping the fuzzy logical relationships, respectively. The last kind model has been widely applied and researched for the reason that it is easy to be understood by the decision makers. To improve the fuzzy time series forecasting model, this paper presents a novel high-order fuzzy time series models denoted asGTS(M,N)on the basis of generalized fuzzy logical relationships. Firstly, the paper introduces some concepts of the generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the proposed model is implemented in forecasting enrollments of the University of Alabama. As an example of in-depth research, the proposed approach is also applied to forecast the close price of Shanghai Stock Exchange Composite Index. Finally, the effects of the number of orders and hierarchies of fuzzy logical relationships on the forecasting results are discussed.

Highlights

In the last two decades, fuzzy time series approach [1,2,3] has been widely used for its power of dealing with imprecise knowledge variables in decision making
To improve the fuzzy time series forecasting model, this paper presents a novel high-order fuzzy time series models denoted as GTS(M,N) on the basis of generalized fuzzy logical relationships
Lee et al [19,20,21,22] presented several models based on the fuzzy time series, genetic algorithm, simulated annealing algorithm, and type2 fuzzy set to forecast temperature and TAIFEX

Summary

Introduction

In the last two decades, fuzzy time series approach [1,2,3] has been widely used for its power of dealing with imprecise knowledge variables in decision making. Lee et al [19,20,21,22] presented several models based on the fuzzy time series, genetic algorithm, simulated annealing algorithm, and type fuzzy set to forecast temperature and TAIFEX. The forecasting procedure and principles are obvious and clear to fuzzy time series researchers and easy to be understood by the decision makers. For these reasons, this paper proposes a high-order fuzzy time series model based on generalized fuzzy logical relationships [38].

Preliminaries

Proposed Model

Empirical Analysis