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PREDICTIVE ANALYTICS FOR CRUDE OIL PRICE USING RNN�LSTM NEURAL NETWORK

Zaidi, Ahmad Naqib (2019) PREDICTIVE ANALYTICS FOR CRUDE OIL PRICE USING RNN�LSTM NEURAL NETWORK. IRC, Universiti Teknologi PETRONAS. (Submitted)

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Abstract

Predictions on stock market prices are a great challenge due to the fact that it is an immensely complex, chaotic and dynamic environment. There are many studies from various areas aiming to take on that challenge and Machine Learning approaches have been the focus of many of them. There are many examples of Machine Learning algorithms been able to reach satisfactory results when doing that type of prediction. This article studies the usage of LSTM networks on that scenario, to predict future trends of stock prices based on the price history, alongside with technical analysis indicators. For that goal, a prediction model was built, and a series of experiments were executed and theirs results analyzed against a number of metrics to assess if this type of algorithm presents and improvements when compared to other Machine Learning methods and investment strategies. The results that were obtained are promising, predicting if the price of a particular stock is going to go up or not in the near future.

Item Type: Final Year Project
Academic Subject : Academic Department - Information Communication Technology
Subject: Q Science > Q Science (General)
Divisions: Sciences and Information Technology > Computer and Information Sciences
Depositing User: Ahmad Suhairi Mohamed Lazim
Date Deposited: 09 Sep 2021 19:58
Last Modified: 09 Sep 2021 19:58
URI: http://utpedia.utp.edu.my/id/eprint/20882

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