LSTM Architecture for Oil Stocks Prices Prediction

Oil companies are among the largest companies in the world whose economic indicators in the global stock market have a great impact on the world economy and market due to their relation to gold, crude oil, and the dollar. To quantify these relationships, we use correlation features and relationships between stocks with the dollar, crude oil, gold, and stock indices of major oil companies, create data sets, and perform continuous and discrete correlation analyses with each other. To predict the stocks of different companies, we use Recurrent Neural Networks (RNNs) and LSTM, because these stocks change in time series. We carry out empirical experiments and perform on the stock indices dataset to evaluate the prediction performance in terms of several common error metrics such as Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The received results are promising and present a reasonably accurate prediction for the price of oil companies' stocks in the near future. Despite the volatility of the investigated systems in continuous and discrete correlation analysis, LSTM has a high interpretability ability to investigate surprising.