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 Conference Papers 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/. Title A name given to the resource Stock Price Prediction Using RNNs: A Comparative Analysis of RNN, LSTM, GRU, and BiRNN Subject The topic of the resource BiRNN; GRU; LSTM; MAE; MAPE; MSE; Nifty 50 Forecasting; RNN; Stock Price Prediction Description An account of the resource Stock price prediction is a crucial area of financial market research, having significant implications for investors, traders, and analysts. However, given the dynamic and intricate nature of financial markets - which are impacted by a wide range of variables such as economic statistics, geopolitical developments, and market sentiment - accurately projecting stock prices is intrinsically difficult. Conventional techniques frequently fail to fully capture these dynamics, producing predictions that are not ideal. Recurrent Neural Networks (RNNs), one of the most recent developments in machine learning, provide potential methods to overcome these obstacles. Despite their potential, the effectiveness of different RNN architectures in stock price prediction remains an area of active research. This study compares four Recurrent Neural Network (RNN) architectures - Simple RNN, Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional Recurrent Neural Network (BiRNN) - for forecasting the Nifty 50 index values on the Indian National Stock Exchange (NSE) from the year 2000 to 2021. Using a comprehensive dataset spanning two decades, we assess each model's performance using the metrics Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The data shows that the BiRNN model regularly outperforms the other models in all criteria i.e., MAE, MAPE, and MSE, indicating higher predictive accuracy. This study adds to the existing research by offering useful insights into the usefulness of RNN models, especially that of the BiRNN model for predicting stock prices, specifically in the setting of the Nifty 50 index. Our findings emphasize BiRNN's potential as a stock price prediction model and open new options for future research in this area. 2024 IEEE. Creator An entity primarily responsible for making the resource Agarwal S.; Alapatt B.P.; Nair A.M.; George F.J. Source A related resource from which the described resource is derived 2nd International Conference on Sustainable Computing and Smart Systems, ICSCSS 2024 - Proceedings, pp. 1074-1079. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICSCSS60660.2024.10625047" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICSCSS60660.2024.10625047</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203107322&amp;doi=10.1109%2FICSCSS60660.2024.10625047&amp;partnerID=40&amp;md5=d3f0f2561efbd90e4222feeba49f129f" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203107322&amp;doi=10.1109%2fICSCSS60660.2024.10625047&amp;partnerID=40&amp;md5=d3f0f2561efbd90e4222feeba49f129f</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835037999-0 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Agarwal S., School of Sciences, CHRIST(Deemed to Be University), Delhi NCR, India; Alapatt B.P., School of Sciences, CHRIST(Deemed to Be University), Delhi NCR, India; Nair A.M., School of Business and Management, CHRIST(Deemed to Be University), Delhi NCR, India; George F.J., CHRIST(Deemed to Be University), Delhi NCR, India