Enhanced Accuracy of High – Order Fuzzy Time Series Forecasting Model Based on Harmony Search Algorithm

In recent years, many fuzzy time series models have already been used to solve nonlinear and complexity issues. However, first-order fuzzy time series models have proven to be insufficient for solving these problems. For this reason, many researchers have been proposed high-order fuzzy time series model to improve the forecasting accuracy. From this viewpoint. This paper presents a high-order forecasting model based on fuzzy time series (FTS) and harmony search algorithm to overcome the drawbacks above. Firstly, a forecasting model is constructed from the high – order fuzzy logical relationship. Following, the harmony search algorithm is combined with FTS model to adjust the lengths of each interval and find optimal interval in the universe of discourse with an intend to increase forecasting accuracy. To illustrate the forecasting process and the usefulness of the proposed model, two numerical datasets like average rice production of Viet Nam and the enrolments of the University of Alabama are used. The application results accentuate the superiority of the proposed model over the other models for forecasting the enrolments of the University of Alabama.