INTELLIGENT STOCK FORECASTING USING LSTM

INTELLIGENT STOCK FORECASTING USING LSTM

Mrs. R. Jenifer #1, Shalini S#2, Siva Shalini S#3, Navin Prasanth S#4

#1Professor, Department of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India.

#2, #3, #4 UG Student, Department of Computer Science and Engineering, Sri Shakthi Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India.

R.Jenifer, Shalini S, Siva Shalini S, Navin Prasanth S

I.      ABSTRACT:
An intelligent stock market forecasting using LSTM platform is designed to assist investors, analysts, and financial enthusiasts in making informed trading decisions using advanced deep learning techniques. Unlike traditional forecasting approaches that depend on statistical models, this system utilizes Long Short-Term Memory (LSTM) networks to capture temporal patterns in historical stock price data, enabling accurate predictions. The platform analyses key financial indicators such as historical prices, trading volume, and market trends to generate future price forecasts. By learning from sequential data, the LSTM model effectively handles market volatility and long-term dependencies, providing meaningful insights into stock behaviour. In addition to numerical data, the system incorporates Natural Language Processing (NLP) techniques to enhance prediction accuracy by analyzing financial news, social media, and market reports. Text data is processed using tokenization, stop-word removal, and sentiment analysis to classify information as positive, negative, or neutral, which is integrated with time-series data. The platform features an intuitive interface that allows users to visualize stock trends, predicted values, and performance metrics. Users are provided with a personalized dashboard to monitor selected stocks and analyse historical performance. Secure authentication mechanisms protect user data through encryption and session management. To ensure reliability, the system continuously updates its model with new market data, improving prediction accuracy over time. By combining deep learning and NLP-based sentiment analysis, the platform simplifies financial data and supports smarter investment decisions.

Keywords: Stock Market Prediction, LSTM (Long Short-Term Memory), Deep Learning, Time Series Forecasting, Financial Analytics.