AUTOMATIC CLUSTERING AND OPTIMIZED FUZZY LOGICAL RELATIONSHIPS FOR MINIMUM LIVING NEEDS FORECASTING

Yusuf Priyo Anggodo, Wayan Firdaus Mahmudy

Abstract


Forecasting of minimum living needs is useful for companies in financial planning next year. In this study, the firescasting is done using automatic clustering and optimized fuzzy logical relationships. Automatic clustering is used to form a sub-interval time series data. Particle swarm optimization is used to set and optimze interval values in fuzzy logical relationships. The data used as many as 11 years of historical data from 2005-2015. The optimal value of the test results obtained by the p = 4, the number of iterations = 100, the number of particles = 45, a combination of Vmin and Vmax = [-0.6, 0.6], as well as combinations Wmax and Wmin = [0, 4, 0 , 8]. These parameters values produce good forecasting results.
Keywords: minimum living needs, automatic clustering, particle swarm optimization, fuzzy logical relationships

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