Crude Oil Price Prediction Using Machine Learning LSTM.

This research paper explores the innovative application of Long Short-Term Memory (LSTM) neural networks with minimal input features for crude oil price forecasting. The research encompasses a comprehensive examination of the model's architecture, training process, data preprocessing, and comparative analysis against actual values. The use of date as the sole input feature challenges conventional practices and highlights the model's ability to capture intricate temporal dependencies and seasonality. The research emphasizes the LSTM model's accuracy, robustness, adaptability, and data efficiency, demonstrating its potential for wider applicability across financial markets. This work advances the role of deep learning in financial forecasting, introduces a minimalist approach to modeling, and fosters the fusion of minimalism and deep learning. The overarching contribution lies in shaping the future of predictive modeling and decision-making in the complex and dynamic world of financial markets.