Forecasting gold price using autoregressive integrated moving average and artificial neural network model

Abd Wahab, Nursu'aidah (2016) Forecasting gold price using autoregressive integrated moving average and artificial neural network model. Masters thesis, Universiti Tun Hussein Onn Malaysia.

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Abstract

The main objective of this study is to predict monthly price of gold. The monthly sample data of gold price )in RM per ounce) were taken from January 2004 to November 2015. In this study, autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) model was used to forecast the future price of gold. Three different back propagation algorithms of ANN model will be use throughout this study which is consist of resilient back propagation (RBP), scaled conjugate gradient back propagation (SCGBP) and Levenberg-Marquardt back propagation (LMBP) algorithm. The model performance was evaluated in term of error magnitude (EM) and directional change error (DCE). The result of the study had suggested that the ANN model with LMBP algorithm had perform the best model compared to ARIMA model and another ANN model using RBP and SCGBP algorithm.

Item Type:Thesis (Masters)
Subjects:Q Science > QA Mathematics > QA76 Computer software
ID Code:8998
Deposited By:Mr. Mohammad Shaifulrip Ithnin
Deposited On:03 Apr 2017 15:26
Last Modified:03 Apr 2017 15:26

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