An improved forecasting model combining recurrent fuzzy logical relationships and K-means clustering technique
An improved forecasting model combining recurrent fuzzy logical relationships and K-means clustering technique
In this paper, a new forecasting model based on combining the Fuzzy Time Series (FTS) and K-mean clustering algorithm with two concepts, the recurrent fuzzy relationship groups (RFRGs) and K-mean clustering technique, 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.