Abstract:

To enhance the accuracy of intuitionistic fuzzy time
series forecasting model, this paper analyses the influence of
universe of discourse partition and compares with relevant literature.
Traditional models usually partition the global universe of
discourse, which is not appropriate for all objectives. For example,
the universe of the secular trend model is continuously variational.
In addition, most forecasting methods rely on prior information, i.e.,
fuzzy relationship groups (FRG). Numerous relationship groups
lead to the explosive growth of relationship library in a linear model
and increase the computational complexity. To overcome problems
above and ascertain an appropriate order, an intuitionistic
fuzzy time series forecasting model based on order decision and
adaptive partition algorithm is proposed. By forecasting the vector
operator matrix, the proposed model can adjust partitions and
intervals adaptively. The proposed model is tested on student enrollments
of Alabama dataset, typical seasonal dataset Taiwan
Stock Exchange Capitalization Weighted Stock Index (TAIEX) and
a secular trend dataset of total retail sales for social consumer
goods in China. Experimental results illustrate the validity and applicability
of the proposed method for different patterns of dataset.