Adaptive partition intuitionistic fuzzy time series forecasting model

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.

An adaptive categorical effect size method based on intuitionistic meta fuzzy functions

“…In addition to time series and forecasting methods, are widely used in the field of medicine for clustering images and diagnostics 16 – 18 . Numerous studies employing IFSs have been proposed by Fan et al 19 , Kumar and Gangwar 20 , Lei, et al 21 , Tak 22 , Gwak et al 23 .…”

Network Traffic Anomaly Detection Algorithm Based on Intuitionistic Fuzzy Time Series Graph Mining

“…This greatly expands the time series' ability to process uncertain and incomplete fuzzy information, and effectively improves the forecasting performance of the FTS model [10]. Wang et al studied IFTS forecasting algorithms in terms of domain partitioning, fuzzy relations, forecasting rule development, multivariate, high-order, and long-term forecast, and proposed high-order multivariate [11], variable ordering heuristic [12], adaptive-partitioning [13], and long-term [14] IFTS forecasting models. The models have achieved good forecasting results on typical datasets such as college enrollment, stock index transaction volume, retail sales of consumer goods, and average daily temperature.…”