Transforming oil market analysis: A novel GAN + LSTM predictive framework

A novel method of predicting the crude oil WTI futures prices based on a data set covering April 12, 2009 through January 7, 2024. To capture complex market dynamics more precisely, it incorporates key market factors such as open, high, and low price along with slacked variable inclusions and moving averages over 7, 14, and 30 days. It includes generative adversarial networks augmented with long short-term memory (GAN + LSTM), LSTM, gated recurrent units (GRUs), and artificial neural networks (ANNs) as predictive models, where their performance was compared by a variety of measurements like mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE), normalized root mean square error (NRMSE), and adjusted R-square. The GAN + LSTM model proved its accuracy over others, with the lowest MSE (0.001), MAE (0.029), MAPE (4.639), SMAPE (4.734), NRMSE (0.057), and the highest adjusted R-squared (0.943). This model has been viewed as dominant due to its ability to integrate and gain information from complex data patterns. In volatile markets, the outcome of this study will be of fundamental significance in developing proactive models that will provide the most efficient means of making more informed decisions.