Forecasting with Feed Forward Neural Network model and adaptive simulated annealing algorithm (Case: world crude oil prices that was published by OPEC). (May 2019)

• Record Type: Journal Article
• Authors: Hanafie, A; Sugito, ; Sudarno, ; Hakim, A R
• Is Part Of: Journal of physics. Volume 1217(2019)
• Publication Date: 2019-05
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1217/1/012079
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Today, crude oil trading industry became an important industry in the world, it was caused by highly fuel oil consumption. The price of crude oil has a changing price trend, it makes the prediction of crude oil in the coming periods to be challenging. Various methods can use to forecast price of crude oil, it is using ARIMA Box-Jenkins model with OLS method to estimate the parameter, but this method has several assumptions that must be complete. Overtime, many methods were develop, one of them is artificial neural network can be combine with various parameter optimization methods such as Adaptive Simulated Annealing algorithm. Adaptive Simulated Annealing algorithm is an optimization method it was inspired by the process of crystallization, the advantages of this algorithm has a running time faster than similar algorithms. The combination of artificial neural networks and Adaptive Simulated Annealing algorithms can be used to model the historical data without requiring assumptions in the analysis. Based on the analysis on this research, the best model is obtained FFNN 2-5-1 with MAPE value of 1.0042%.