Document Type : Research Paper
Authors
1 Department of Accounting,, West Tehran Branch, Islamic Azad University, Tehran, Iran.
2 Ph.D. Student of Accounting, Department of Accounting, West Tehran Branch, Islamic Azad University, Tehran, Iran.
Abstract
High fluctuations in the price of crude oil, as the main source of energy and an important raw material of the global chemical industry, has doubled the importance of accurate estimation and forecasting of its price trend in recent years. The purpose of this applied research, is to increase the ability to predict crude oil prices using non-linear patterns by artificial intelligence. For this purpose, four artificial intelligence networks MLP, RNN, LSTM 3366and GRU have been used and their capabilities compared to each other and the benchmark model, besides their prediction accuracy have been evaluated using the mean squared error method. The studied sample is North Sea Brent crude oil data from Aug 1st 2007 to May 31st 2024 on a daily, monthly and yearly basis.
The results of the research indicate that the network architecture in these models have several advantages in extracting information from the data in order to make more accurate predictions, and the time to obtain future prices is shorter and less error-prone. Also, among the selected non-linear models, GRU has more accurate predictions with less error in different frequencies and in a shorter time.
The results of the research indicate that the network architecture in these models has several advantages in extracting information from the data in order to make more accurate predictions, and the time to obtain future prices is shorter and less error-prone.
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