Stock Market Price Prediction Using LSTM

Abstract

Abstract: This research proposes an innovative approach involving the implementation of an LSTM (Long Short-Term Memory) model for forecasting stock prices. The predictive analysis relies on historical data to anticipate future stock movements. The utilization of a Stacked LSTM is advocated for this prediction task, as it effectively incorporates past information, enhancing the accuracy of predictions. The Stacked LSTM model proves advantageous in capturing long-term dependencies within the data, rendering it well-suited for the dynamic and intricate nature of stock market prediction. Following the model's training phase, its efficacy will be evaluated using test data, and subsequently, the model will be applied to forecast stock prices for the upcoming 30 days.