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 Sreya, Pampati; Yashaswi, D.; Stephen, R.; Gobinath, R.; Ramkumar, S. Title A name given to the resource Indian Stock Market Prediction Using Neural Networks: A Comparative Analysis Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1469 LNNS;pp.101-112 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-7807-5_10" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-7807-5_10</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020263014?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020263014?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 Sreya P., CHRIST University, Bangalore, India; Yashaswi D., CHRIST University, Bangalore, India; Stephen R., CHRIST University, Bangalore, India; Gobinath R., CHRIST University, Bangalore, India; Ramkumar S., CHRIST University, Bangalore, India Description An account of the resource Predicting stock prices remains a challenging problem due to the highly dynamic and nonlinear nature of financial markets. Traditional statistical models like ARIMA and GARCH often fail to capture the complexities inherent in stock market data. This paper investigates the use of deep learning techniques, focusing on Convolutional Neural Networks (CNNs) and a hybrid CNN-LSTM ensemble model for stock price prediction in the Indian stock market. The CNN model efficiently extracts temporal patterns from sequential data, while the CNN-LSTM ensemble leverages temporal dependencies for improved long-term prediction accuracy. Historical data from Tata motors, spanning over two decades, was used to train and evaluate the models. Experimental results highlight the CNN-LSTM ensemble's superior performance in capturing volatile trends and long-term dependencies, with a notable decrease in test loss compared to standalone CNN. This study underscores the effectiveness of hybrid deep learning architectures in enhancing prediction reliability, paving the way for more adaptive and robust financial forecasting systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Convolutional Neural Networks (CNNs); Deep learning; Ensemble model; Long Short-Term Memory (LSTM); Machine learning (ML); Stock price prediction Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981967806-8; 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