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@article{176748,
        author = {Shubham Kumar Jha and Rithik Chaudhary and Saurabh Kumar and Vishal Rana},
        title = {Real-Time Stock Price Forecasting Web Application: A Deep Learning Approach with LSTM Network},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7866-7870},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176748},
        abstract = {Predicting stock prices is very difficult due to the unpredictable behaviour of financial markets. We introduce a web application that uses Long Short-Term Memory (LSTM) networks to forecast stock prices in real time. The system continuously retrains its models using historical Yahoo Finance data which let users set custom prediction horizons through recursive forecasting and displays the results with interactive and animated time-series charts built using Chart.js. The system includes a tool that explains the predictions by comparing them to recent average values. Our tests shows that the app accurately predicts stock prices and update its visuals quickly which makes it a very useful tool for investors.},
        keywords = {},
        month = {May},
        }