ENHANCING FINANCIAL PREDICTIONS USING LSTM AND CLOUD TECHNOLOGIES: A DATA-DRIVEN APPROACH

Abstract

The increasing complexity of financial markets necessitates advanced predictive models for informed decision-making. This study explores the integration of Long Short-Term Memory (LSTM) networks and cloud technologies to enhance financial forecasting accuracy and computational efficiency. By leveraging LSTMs' ability to capture long-term dependencies in time-series data and cloud computing's scalable infrastructure, this approach improves model performance while addressing computational constraints. Our results demonstrate that cloud integration optimizes resource utilization, reduces training time, and enhances real-time predictive capabilities. However, challenges such as data noise, security concerns, and real-time prediction reliability remain. The findings suggest that combining advanced preprocessing, hybrid AI models, and secure cloud environments can further refine financial forecasting methodologies.