Forecasting Price of the Crude oil using LSTM based on RNN

• Published in: International journal for research in applied science and engineering technology Vol. 10; no. 7; pp. 196 - 206
• Main Author: C R, Dr. Nirmala
• Format: Journal Article
• Language: English
• Published: 31.07.2022
• ISSN: 2321-9653; 2321-9653
• DOI: 10.22214/ijraset.2022.45094

Oil demand is inelastic; therefore the rise in price is good news for producers because they will see an increase in their revenue. Oil importers, however, will experience increased costs of purchasing oil. Because oil is the largest traded commodity, the effects are quite significant. A rising oil price can even shift economic/political power from oil importers to oil exporters. The crude oil price movements are subject to diverse influencing factors. Our work mainly focuses on applying Recurrent Neural Networks to predict the Crude Oil Price. This decision helps common people to buy crude oil at the proper time. Time series analysis is the best option for this kind of prediction because we are using the previous history of crude oil prices to predict future price of the crude oil. So we would be implementing RNN (Recurrent Neural Network) with LSTM (Long Short Term Memory) to achieve the task. We will be experimenting with different types of models with varying number of epochs, look backs and other tuning methods.