A NOVEL FORECASTING MODEL BASED ON COMBINING TIME-VARIANT FUZZY LOGICAL RELATIONSHIP GROUPS AND K-MEANS CLUSTERING TECHNIQUE

Abstract

Most of previous the forecasting approaches based on fuzzy time series (FTS) used the same length of intervals. The weakness of the static length of intervals is that the historical data are roughly put into intervals, although the variance of them is not high. In this paper, a new forecasting model based on combining the fuzzy time series and K-mean clustering algorithm with three computational methods, K-means clustering technique, the time - variant fuzzy logical relationship groups and defuzzification forecasting rules, is presented. Firstly, the authors use the K-mean clustering algorithm to divide the historical data into clusters and adjust them into intervals with different lengths. Then, based on the new intervals obtained, the proposed method is used to fuzzify all the historical data and create the time -variant fuzzy logical relationship groups based on the new concept of time – variant fuzzy logical relationship group. Finally, Calculate the forecasted output value by the improved defuzzification technique in the stage of defuzzification. To evaluate performance of the proposed model, two numerical data sets are utilized to illustrate the proposed method and compare the forecasting accuracy with existing methods. The results show that the proposed model gets a higher average forecasting accuracy rate to forecast the Taiwan futures exchange (TAIFEX) and enrollments of the University of Alabama than the existing methods based on the first – order and high-order fuzzy time series.