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

Abstract EN

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 as GTS(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.