Using kernel-based fuzzy clustering algorithm for forecasting time series

The paper develops the forecasting model for time series via Kernel-based fuzzy clustering algorithm (KFC). This algorithm not only finds the appropriate number of groups, clustering for time series but also determines the fuzzy relationship of elements inside of time series. One of the most advantages of the proposed method is the improvement of fuzzy clustering problem based on the Kernel function to increase the fuzzy relationship between centroid clusters and elements in series. Experimental results show that the proposed algorithm has more advantages than other ones. In addition, this algorithm also shows potential and effective in comparing with other algorithms. Keywords: Kernel-based fuzzy clustering, Time series, Suitable number of clusters, Machine learning