Fuzzy Time Series: A Realistic Method to Forecast Gross Domestic Capital of India

In the era of uncertainty and chaos, the decision making is a complex process especially when it is related with the future prediction. The decision making process is fully dependent on the level of accuracy in forecasting. It is obvious that forecasting activities play a vital role in our daily life. The classical time series methods can not deal with forecasting problems in which, the values of time series are linguistic terms represented by fuzzy sets [1, 2]. Therefore, Song and Chissom [4] presented the theory of fuzzy time series to overcome the drawback of the classical time series methods. Time series prediction is a very important practical application with a diverse range of applications including economic and business planning, inventory and production control, weather forecasting, signal processing and control, Pattern matching etc. Based on the theory of fuzzy time series, Song presented some forecasting methods [4,5,6] to forecast the enrollments of the University of Alabama. Chen [8] presented a method to forecast the enrollments of the University of Alabama based on fuzzy time series. It has the advantage of reducing the calculation time and simplifying the calculation process. Hwang [10], used the differences of the enrollments to present a method to forecast the enrollments of the University of Alabama based on fuzzy time series. Huang extended Chen’s work presented in [9] and used simplified calculations with the addition of heuristic rules to forecast the enrollments. Chen [12] presented a forecasting method based on high order fuzzy time series for forecasting the enrollments of the University of Alabama. Chen [13] and Hwang presented a method based on fuzzy time series to forecast the temperature.