A Novel Forecasting Based on Automatic-optimized Fuzzy Time Series - Yusuf Priyo Anggodo; Wayan Firdaus Mahmudy (Universitas Ahmad Dahlan, 2018)
In this paper, we propose a new method for forecasting based on automatic-optimized fuzzy time series to forecast Indonesia Inflation Rate (IIR). First, we propose the forecasting model of two-factor high-order fuzzy-trend logical relationships groups (THFLGs) for predicting the IIR. Second, we propose the interval optimization using automatic clustering and particle swarm optimization (ACPSO) to optimize the interval of main factor IIR and secondary factor SF, where SF = Customer Price Index (CPI), the Bank of Indonesia (BI) Rate, Rupiah Indonesia /US Dollar (IDR/USD) Exchange rate, Money Supply. The proposed method gets lower root mean square error (RMSE) than previous methods.