Particle swarm optimization and intuitionistic fuzzy set-based novel method for fuzzy time series forecasting

Many fuzzy time series (FTS) methods have been developed by the researchers without including non-determinacy caused using single function for both membership and non-membership grades. Optimum length of intervals and inclusion of non-determinacy have been two important key issues for the researchers for long time. In this paper, we propose a novel hybrid FTS forecasting method using particle swarm optimization (PSO) and intuitionistic fuzzy set (IFS). PSO determines optimum length and IFS includes non-determinacy during fuzzification of time series data. To show the applicability and suitability of proposed forecasting method, it is implemented on three time series data of the University of Alabama, State bank of India (SBI) share price at Bombay Stock Exchange (BSE), India and car sells in Quebec. Performance of proposed method is measured using mean square error (MSE). Reduced amount of MSE confirms outperformance of proposed FTS forecasting method over various existing fuzzy set, IFS, hesitant and probabilistic hesitant fuzzy set-based FTS forecasting methods in forecasting the University of Alabama enrollments, SBI share price and car sells in Quebec. Validity of the proposed FTS forecasting method is also verified using tracking signal.