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FTS is popular in many recent years. Researchers are competing to outperform existing method by making new improvement including modifications at clustering step. Here we discuss about clustering process, i.e., partitioning based metric frequency density and firefly clustering algorithm. In the simulation, we compare the forecasting results and error value of the method with previous existing methods. The modifications give better forecasting results than previous methods indicated with smaller Root Means Errors (RMSEs) and Average Forecasting Error (AFER).

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Determining the effective number of clusters can influence forecasting results in FTS method’s applications. Unfortunately, the issue of how many clusters should be used to improve forecasting results has not been touched in previous researches. We observe for some different number of clusters and compare its Root Mean Squared Errors (RMSEs) results. The numerical simulation using Jakarta Composite Index (IHSG) data and the forecasting results show that RMSEs value decrease when the number of clusters is increased. The RMSEs value drops significantly when n = 50 clusters are used.