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@article{187940,
        author = {Yuktha Mukhi S P and Dr.Hemanth T S and Chandana H R and Divyashree H A and Haleema Sadiya},
        title = {Comparative Analysis Using ML and DL Models for Time Series stock price prediction stock price prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {196-201},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187940},
        abstract = {Stock market forecasting has been a hard issue since data- driven strategies appeared. Vital methods for forecasting stocks are DL and ML, which have turned into time-series modeling. In this review article, ML and DL models are compared and discussed in detail. Strengths, weak points, discusses, also Applications within real-world finance forecasting. The models that are classical in ML regress in a linear way. A random forest and a support vector machine are examples. Classical models in DL do include recurrent neural networks as well as long short term memory plus gated recurrent units This endeavor gives the perceptions under which models prove most effective. It highlights different market conditions and also scenarios about data.},
        keywords = {include Machine Learning (ML), Deep Learning (DL), Linear Regression, Decision Tree, Random Forest, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN).},
        month = {November},
        }