Comparison of ARIMA model and Artificial Neural Network in forecasting gold price

Developing an accurate model of gold price is crucial as gold price have a great effect on the investment decisions of individuals, corporations and countries. The purpose of this study is to compare the performance of model Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Netw...

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Main Authors: Uh, Bing Hong, Noriza Majid
Format: Article
Language:English
Published: Penerbit Universiti Kebangsaan Malaysia 2021
Online Access:http://journalarticle.ukm.my/17936/
http://journalarticle.ukm.my/17936/1/Paper-4-Noriza.pdf
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author Uh, Bing Hong
Noriza Majid,
author_facet Uh, Bing Hong
Noriza Majid,
author_sort Uh, Bing Hong
building UKM Institutional Repository
collection Online Access
description Developing an accurate model of gold price is crucial as gold price have a great effect on the investment decisions of individuals, corporations and countries. The purpose of this study is to compare the performance of model Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) in gold price forecasting based on the value of root mean squared error (RMSE). Daily gold price data collected from World Gold Council dated from 3 September 2018 to 30 October 2020 is used in this study. ARIMA (4,1,0) is chosen as the best model for the time series model based on Akaike Information Criterion (AIC). Long short-term memory (LSTM) has been chosen as artificial neural network’s method to forecast the gold price. After comparing multiple step forecasting and one step ahead forecasting using ARIMA and LSTM, it is found that LSTM has smaller RMSE as compared to ARIMA. The result in this paper show that the ANN model outperforms ARIMA model in forecasting gold price.
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spelling oai:generic.eprints.org:179362022-01-13T03:55:01Z http://journalarticle.ukm.my/17936/ Comparison of ARIMA model and Artificial Neural Network in forecasting gold price Uh, Bing Hong Noriza Majid, Developing an accurate model of gold price is crucial as gold price have a great effect on the investment decisions of individuals, corporations and countries. The purpose of this study is to compare the performance of model Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) in gold price forecasting based on the value of root mean squared error (RMSE). Daily gold price data collected from World Gold Council dated from 3 September 2018 to 30 October 2020 is used in this study. ARIMA (4,1,0) is chosen as the best model for the time series model based on Akaike Information Criterion (AIC). Long short-term memory (LSTM) has been chosen as artificial neural network’s method to forecast the gold price. After comparing multiple step forecasting and one step ahead forecasting using ARIMA and LSTM, it is found that LSTM has smaller RMSE as compared to ARIMA. The result in this paper show that the ANN model outperforms ARIMA model in forecasting gold price. Penerbit Universiti Kebangsaan Malaysia 2021 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/17936/1/Paper-4-Noriza.pdf Uh, Bing Hong and Noriza Majid, (2021) Comparison of ARIMA model and Artificial Neural Network in forecasting gold price. Journal of Quality Measurement and Analysis, 17 (2). pp. 31-39. ISSN 1823-5670 https://www.ukm.my/jqma/current/
spellingShingle Uh, Bing Hong
Noriza Majid,
Comparison of ARIMA model and Artificial Neural Network in forecasting gold price
title Comparison of ARIMA model and Artificial Neural Network in forecasting gold price
title_full Comparison of ARIMA model and Artificial Neural Network in forecasting gold price
title_fullStr Comparison of ARIMA model and Artificial Neural Network in forecasting gold price
title_full_unstemmed Comparison of ARIMA model and Artificial Neural Network in forecasting gold price
title_short Comparison of ARIMA model and Artificial Neural Network in forecasting gold price
title_sort comparison of arima model and artificial neural network in forecasting gold price
url http://journalarticle.ukm.my/17936/
http://journalarticle.ukm.my/17936/
http://journalarticle.ukm.my/17936/1/Paper-4-Noriza.pdf