Utilizing Deep Learning Techniques for Crude Oil Prices Forecasting
DOI:
https://doi.org/10.56536/jicet.v4i2.149Abstract
The price of crude oil has a major effect on the global economy. Over the previous few years, the price of crude oil has fluctuated more than the price of any other commodity. Since the price of crude oil is based on several outside variables and significant volatility Predicting the price of crude oil is extremely difficult. Recurrent neural network-based Long Short-Term Memory (LSTM) has demonstrated superior performance in predicting highly volatile prices. This model is used to assess and predict major crude oil prices. The useful data from the WTI unrefined petroleum markets is used to evaluate the suggested model's display. The preliminary results show that the proposed model delivers increases in the predicted level of outcome precision.The necessary information is taken from the official website of the Federal Reserve Bank of St. Louis to generate an output of 0.951826 for accuracy and 0.041717 for RMSE errors based on the 100-epoch LSTM.