Paper Contents
Abstract
These days, the stock market is rising. This study provides a new approach to market and stock price prediction using Python-based Long Short-Term Memory (LSTM) deep learning models. To detect the stock prize and capture temporal correlations, the research utilises LSTM architecture. The LSTM model is trained and assessed using a large, preprocessed dataset of several stock markets. Optical flow techniques or deep learning-based algorithms are used to extract robust features. These features are then used to train the LSTM model. Transfer learning makes use of large action recognition datasets to apply trained models. When measured in terms of accuracy, precision, recall, and F1-score, the LSTM model performs well, handling the temporal components of stock reward with great accuracy. Because of its real-time performance, it can be used for assistive devices and real-time interpretation assistance.
Copyright
Copyright © 2024 Er. Shaweta Mahey, Er. Manpreet Singh. This is an open access article distributed under the Creative Commons Attribution License.
