Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sao, Ameet; Verma, Rajesh; Lokannadha, Irala; Nagarajan, S.; Mary, S. Suma Christal; Raj, Infant Title A name given to the resource Predictive Analytics for Stock Price Forecasting: Machine Learning Techniques in Financial Markets Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Conference on Intelligent Systems and Computational Networks, ICISCN 2025; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICISCN64258.2025.10934289" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICISCN64258.2025.10934289</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105002687206?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105002687206?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sao A., Amity University, Rics School of Built Environment, Noida, India; Verma R., Christ (Deemed to be University), School of Business and Management, Ghaziabad, India; Lokannadha I., University of Hyderabad, School of Management Studies, Hyderabad, India; Nagarajan S., Bannari Amman Institute of Technology, Department of School of Management Studies, Erode, India; Mary S.S.C., Panimalar Engineering College, Department of Information Technology, Chennai, India; Raj I., K.Ramakrishnan College of Engineering, Department of Cse, Trichy, India Description An account of the resource Stock market forecasting is significantly challenging because financial data generally exhibits non-linearity, and volatility is highly presented. Traditional methods such as the ARIMA model and NN fail to take a good grasp of intricate and complex temporal patterns in changes related to market trends. By overcoming these limitations, it makes use of LSTM and combines GAN networks. The LSTM exploits the historical stock price data for temporal dependencies, whereas GAN produces realistic synthetic data to augment model training. The Stock Market Dataset was used, and the proposed model was implemented using Python with TensorFlow and PyTorch frameworks. The hybrid LSTM-GAN model resulted in better performance with an RMSE of 0.0125, MAE of 0.0093, and R2 of 0.926, thus outperforming LSTM and traditional forecasting models. This work greatly enhances the accuracy of forecasting, avoids overfitting, and promotes performance in volatile market environments. The results are extremely useful for investors, financial analysts, and trading platforms because they can make better predictions. 2025 IEEE. Subject The topic of the resource financial market volatility; generative adversarial networks; LSTM-GAN hybrid model; stock price forecasting; time-series prediction Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833152924-6; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online