PROBABILISTIC AND INTUITIONISTIC FUZZY SETS–BASED METHOD FOR FUZZY TIME SERIES FORECASTING

Abstract

The present study proposes integration of cumulative probability distribution approach for partitioning the universe of discourse along with intuitionistic fuzzy sets to include a nondeterminacy index to enhance the accuracy in forecasted values in fuzzy time series forecasting. The proposed model is a simplified computational approach that uses characteristics of normal distribution to define the length of a linguistic interval and the degree of nondeterminacy to establish fuzzy logical relations on the time series data. The proposed method has been implemented to forecast the movement of share market prices of State Bank of India on the Bombay Stock Exchange, India. The suitability of the developed model has been examined in comparison with other models in terms of mean square and average forecasting errors to show its superiority.