A Novel Forecasting Based on Automatic-optimized Fuzzy Time Series
| Autor: | Wayan Firdaus Mahmudy, Yusuf Priyo Anggodo |
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| Rok vydání: | 2018 |
| Předmět: |
Mathematical optimization
Mean squared error Particle swarm optimization Interval (mathematics) Fuzzy logic Automatic-optimized Fuzzy logical relationship Logical relationship groups Exchange rate Price index Electrical and Electronic Engineering Cluster analysis Two-factor high-order fuzzy-trend Infinite impulse response Similarity measures Mathematics |
| Popis: | 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 highorder 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. |
| Databáze: | OpenAIRE |
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