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@article{206774,
        author = {Ravishankar and Krishna and Raksha and Neetha},
        title = {Stock Price Prediction Using LSTM Deep Learning Method},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {414-418},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206774},
        abstract = {Forecasting stock market movements is inherently complex dynamic and non-linear behavior, and global events. Accurate forecasting is when evaluating opportunities and managing institutions to make sound judgments and minimize risks. Classic statistical methods often find it difficult to capture complex temporal dependencies and hidden patterns present in stock market data. This research proposes a deep learning– based which are specifically designed to capture long-term relationships and trends in time-series data model sequential and time-series data. A proposed system utilizes and technical indicators that provide deeper insights into price movements, market trends, to develop the LSTM model. The model captures long-term relationships and patterns in the data, enabling it to generate more accurate future price predictions. such as data cleaning, normalization, and feature engineering help enhance the quality of in such as data cleaning, normalization, and feature engineering help enhance the quality of input data. normalization and feature scaling are applied to refine the model’s output performance. Financial Forecasting, Artificial Intelligence, Market Analysis Time Series Forecasting, Financial Data Analysis, Neural Networks},
        keywords = {.},
        month = {July},
        }