Foresight In Finance: Elevating Predictions With Enhanced Rnn-Lstm And Adam Optimizer

Abstract

In the ever-evolving landscape of financial markets, accurate stock price predictions play a pivotal role in informed decision-making. This paper explores the application of advanced deep learning techniques to enhance the precision of future stock data predictions. Specifically, research focus on leveraging the power of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) architectures, bolstered by the adaptive learning capabilities of the Adam optimizer. The evaluation metrics include Mean Absolute Error and Root Mean Squared Error, providing insights into the model's accuracy and robustness. The findings of this paper contribute to the ongoing discourse on the application of deep learning in finance, offering a promising avenue for refining stock price predictions. As financial markets continue to demand sophisticated forecasting methodologies, the Elevated RNN-LSTM with Adam Optimizer emerges as a valuable tool for stakeholders seeking foresight and precision in their financial decision-making processes.