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@article{176514,
        author = {Mansi and Himanshu Sharma and Naveen Singh and Jayshree Surolia},
        title = {ForecastNet: A Predictive Framework for Stock Market Trends},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {6107-6112},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176514},
        abstract = {This research presents a Stock Price Prediction System that utilizes the principles of machine learning and sentiment analysis in order to predict stock prices for the future. The prediction generated by the system has greater accuracy because it merges historical market data, technical indicators and real-time news sentiment perceived by the market using machine learning models such as LSTM and Random Forest. The proposed solution is cloud-based, scalable, and has a user-friendly platform for analysts and investors to operate. The system gets data, analyzes data and visualizes information, which has the potential to significantly ease decision making in volatile financial markets.},
        keywords = {Stock Prediction, LSTM, Machine Learning, Sentiment Analysis, Technical Indicators, Financial Forecasting, Time-Series Analysis, Deep Learning.},
        month = {April},
        }