A modified weighted method of time series forecasting in intuitionistic fuzzy environment

In this paper, we present a modified weighted method of time series forecasting using intuitionistic fuzzy sets. The proposed weighted method provides a better approach to extent of the accuracy in forecasted outputs. As it is established that the length of interval plays a crucial role in forecasting the historical time series data, so a new technique is proposed to define the length of interval and the partition of the universe of discourse into unequal length of intervals. Further, triangular fuzzy sets are defined and obtain membership grades of each datum in historical time series data to their respective triangular fuzzy sets. Based on the score and accuracy function of intuitionistic fuzzy number, the historical time series data is intuitionistic fuzzified and assigned the weight for intuitionistic fuzzy logical relationship groups. Defuzzification technique is based on the defined intuitionistic fuzzy logical relationship groups and provides better forecasting accuracy rate. The proposed method is implemented to forecast the enrollment data at the University of Alabama and market share price of SBI at BSE India. The results obtained have been compared with other existing methods in terms of root mean square error and average forecasting error to show the suitability of the proposed method.