Predictive Analytics for Crude Oil Price Using RNN-LSTM Neural Network

Prediction of future crude oil price is considered a significant challenge due to the extremely complex, chaotic, and dynamic nature of the market and stakeholder's perception. The crude oil price changes every minute, and millions of shares ownerships are traded everyday. The market price for...

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Main Authors: Aziz, N., Abdullah, M.H.A., Zaidi, A.N.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.29875 /
Published: Institute of Electrical and Electronics Engineers Inc. 2020
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097569472&doi=10.1109%2fICCI51257.2020.9247665&partnerID=40&md5=ba75e31e551363f66c247bf208f82a72
http://eprints.utp.edu.my/29875/
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spelling utp-eprints.298752022-03-25T03:05:11Z Predictive Analytics for Crude Oil Price Using RNN-LSTM Neural Network Aziz, N. Abdullah, M.H.A. Zaidi, A.N. Prediction of future crude oil price is considered a significant challenge due to the extremely complex, chaotic, and dynamic nature of the market and stakeholder's perception. The crude oil price changes every minute, and millions of shares ownerships are traded everyday. The market price for commodity such as crude oil is influenced by many factors including news, supply-and-demand gap, labour costs, amount of remaining resources, as well as stakeholders' perception. Therefore, various indicators for technical analysis have been utilized for the purpose of predicting the future crude oil price. Recently, many researchers have turned to machine learning approached to cater to this problem. This study demonstrated the use of RNN-LSTM networks for predicting the crude oil price based on historical data alongside other technical analysis indicators. This study aims to certify the capability of a prediction model built based on the RNN-LSTM network to predict the future price of crude oil. The developed model is trained and evaluated against accuracy matrices to assess the capability of the network to provide an improvement of the accuracy of crude oil price prediction as compared to other strategies. The result obtained from the model shows a promising prediction capability of the RNN-LSTM algorithm for predicting crude oil price movement. © 2020 IEEE. Institute of Electrical and Electronics Engineers Inc. 2020 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097569472&doi=10.1109%2fICCI51257.2020.9247665&partnerID=40&md5=ba75e31e551363f66c247bf208f82a72 Aziz, N. and Abdullah, M.H.A. and Zaidi, A.N. (2020) Predictive Analytics for Crude Oil Price Using RNN-LSTM Neural Network. In: UNSPECIFIED. http://eprints.utp.edu.my/29875/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Prediction of future crude oil price is considered a significant challenge due to the extremely complex, chaotic, and dynamic nature of the market and stakeholder's perception. The crude oil price changes every minute, and millions of shares ownerships are traded everyday. The market price for commodity such as crude oil is influenced by many factors including news, supply-and-demand gap, labour costs, amount of remaining resources, as well as stakeholders' perception. Therefore, various indicators for technical analysis have been utilized for the purpose of predicting the future crude oil price. Recently, many researchers have turned to machine learning approached to cater to this problem. This study demonstrated the use of RNN-LSTM networks for predicting the crude oil price based on historical data alongside other technical analysis indicators. This study aims to certify the capability of a prediction model built based on the RNN-LSTM network to predict the future price of crude oil. The developed model is trained and evaluated against accuracy matrices to assess the capability of the network to provide an improvement of the accuracy of crude oil price prediction as compared to other strategies. The result obtained from the model shows a promising prediction capability of the RNN-LSTM algorithm for predicting crude oil price movement. © 2020 IEEE.
format Conference or Workshop Item
author Aziz, N.
Abdullah, M.H.A.
Zaidi, A.N.
spellingShingle Aziz, N.
Abdullah, M.H.A.
Zaidi, A.N.
Predictive Analytics for Crude Oil Price Using RNN-LSTM Neural Network
author_sort Aziz, N.
title Predictive Analytics for Crude Oil Price Using RNN-LSTM Neural Network
title_short Predictive Analytics for Crude Oil Price Using RNN-LSTM Neural Network
title_full Predictive Analytics for Crude Oil Price Using RNN-LSTM Neural Network
title_fullStr Predictive Analytics for Crude Oil Price Using RNN-LSTM Neural Network
title_full_unstemmed Predictive Analytics for Crude Oil Price Using RNN-LSTM Neural Network
title_sort predictive analytics for crude oil price using rnn-lstm neural network
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2020
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097569472&doi=10.1109%2fICCI51257.2020.9247665&partnerID=40&md5=ba75e31e551363f66c247bf208f82a72
http://eprints.utp.edu.my/29875/
_version_ 1741197313148190720
score 11.62408

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