Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{194731,
        author = {YAMA SAI AKSHAY and BOPPANA BALA SAI and PULKIT DWIVEDI},
        title = {TIME SERIES FORECASTING},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {5637-5645},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194731},
        abstract = {This project tackles the complex task of predicting stock market prices using advanced techniques like LSTM neural networks, historical data, and technical indicators, with a specific focus on the technology sector. It recognizes the ongoing challenge of achieving accurate predictions in the dynamic world of finance and aims to develop a robust LSTM model for this purpose. The study’s scope is confined to historical price data and technical indicators, while being mindful of potential limitations. The project’s significance lies in its potential to empower investors and financial institutions with more informed decision-making tools, contributing to a better understanding of financial market dynamics. The project structure includes a comprehensive literature review, a detailed research methodology encompassing data collection and preprocessing, model development, and experimental setup. The results and discussion section assesses the model’s performance and discusses its limitations and future research possibilities. Ultimately, this project seeks to bridge the gap between predictive analytics and financial markets, offering a practical contribution to the field of stock price forecasting, benefiting both individual investors and financial professionals.},
        keywords = {Deep Learning, LSTM, Machine Learning, time series analysis and Historical stock data.},
        month = {March},
        }