Lstm stock prediction pdf. It includes steps such as data acquisition, preprocessing, sentiment integration, model training, prediction, and evaluation. - History for AI Project (Team-09)_compressed. The proposed LSTM-based model achieved a high prediction accuracy of up to 97. edu Abstract—Numerous economic, political, and social factors make stock price predictions challenging and unpredictable. Expand 12 PDF 2 Excerpts Jan 19, 2026 · This study aims to predict the stock price movements of Mongolian companies listed on the Mongolian Stock Exchange (MSE) by comparing three effective deep learning models: Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Generative Adversarial Networks (GAN). The results demonstrate that the prediction accuracy of the model with additional sentiment factors is significantly enhanced compared to the model using only stock price data. This paper focuses on developing an artificial intelligence (AI) model for stock price prediction. Predicting stock prices has been a difficult task for many researchers and analysts. Dec 20, 2025 · The proposed model addresses the limitations of traditional prediction methods when handling multi-dimensional, non-linear time series data by capturing long-term temporal dependencies through hierarchical LSTM layers and identifying critical time points and indicator weights via attention mechanisms. The-Quant-Prep / 02_quantitative_data_analysis / misc_notebooks / ml_in_finance / stock_prediction_lstm / multivariate_with_rnn.