Intuitionistic fuzzy set plays a vital role in data analysis and decision-making problems. In this paper, we propose an enhanced and versatile method of forecasting using the concept of intuitionistic fuzzy time series (FTS) based on their score function. The developed method has been presented in the form of simple computational steps of forecasting instead of complicated max–min compositions operator of intuitionistic fuzzy sets to compute the relational matrix [Formula: see text]. Also, the proposed method is based on the maximum score and minimum accuracy function of intuitionistic fuzzy numbers (IFNs) to fuzzify the historical time series data. Further intuitionistic fuzzy logical relationship groups are defined and also provide a forecasted value and lies in an interval and is more appropriate rather than a crisp value. Furthermore, the proposed method has been implemented on the historical student enrollments data of University of Alabama and obtains the forecasted values which have been compared with the existing methods to show its superiority. The suitability of the proposed model has also been examined to forecast the movement of share market price of State Bank of India (SBI) at Bombay Stock Exchange (BSE). The results of the comparison of MSE and MAPE indicate that the proposed method produces more accurate forecasting results.

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The study of fuzzy time series models have been extensively used to improve the accuracy rates in forecasting problems. In this paper, we present a new type 2 fuzzy time series forecasting model based on three-factors fuzzy logical relationship groups. The proposed method uses a new technique to define partitions the universe of discourse into different length of intervals for different factors. Also, the proposed method fuzzifies the historical data sets of the main factors, second factors and third factors to their maximum membership grades obtained by their corresponding triangular fuzzy sets and construct the fuzzy logical relationship groups which is based on the three-factors to enhance in the forecasting accuracy rates. This paper introduces a new defuzzification technique based on their frequency occurrences of fuzzy logical relationships in fuzzy logical relationship groups. The fitness of the propose method is verified in the forecasting of Bombay Stock Exchange (BSE) Sensex historical data and compare in terms of root mean square and average forecasting errors which indicates that the proposed method produce more accurate forecasted output over the existing models in fuzzy time series.