Stock Price Prediction Using LSTM

Abstract

Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, favourite product of people for investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. Designing robust and accurate predictive models for stock price prediction has been an active area of research over a long time. Developing an accurate stock prediction method can help investors in making profitable decisions by reducing the investment risks. Our proposition includes a regression model built on long-and-short-term memory (LSTM) network-based predictive models. These spatial features are then fed into LSTM layers, which capture temporal dependencies and long-range correlations in the time series data. The combination of these two architectures enhances the model's ability to capture both short-term fluctuations and long-term trends in stock prices. This system will provide accurate outcomes in comparison to currently available stock price predictor algorithms. The network is trained and evaluated with various sizes of input data to urge the graphical outcomes. This system will provide accurate outcomes in comparison to currently available stock price predictor algorithms Keywords: Long and Short-Term Memory, Time-Series Data, Recurrent Neural Network.