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 Comparisons of Stock Price Predictions Using Stacked RNN-LSTM Subject The topic of the resource Interest patterns; Investments; Long Short-Term Memory (LSTM); Machine learning; Neural networks; RNN; Stock market Description An account of the resource This paper seeks to identify how the RNN-LSTM can be used in predicting the rise and fall in stock markets thereby helping investors to understand stock prices. Therefore, by predicting the nature of the stock market, investors can use different machine learning techniques to understand the process of selecting the appropriate stock and enhance the return investments thereafter. Long Short-Term Memory (LSTM) is a deep learning technique that helps to analyze and predict the data with respect to the challenges, profits, investments and future performance of the stock markets. The research focuses on how neural networks can be employed to understand price changes, interest patterns and trades in the stock market sector.The datasets of companies such as IBM, Cisco, Microsoft, Tesla and GE were used to build the stacked RNN-LSTM model using timesteps of 7 and 14days. The two layered stacked RNN-LSTM models of the companies such as Microsoft and Tesla achieved their highest model accuracies after being trained over a span of one year whereas the other companies acquired their highest accuracies after being trained over a span of 4 to 5years which implies that the rate of change of economic factors affecting Microsoft and Tesla over a short span of time is high as compared to the other existing companies. 2021, Springer Nature Switzerland AG. Creator An entity primarily responsible for making the resource Sequeira S.; Banu P.K.N. Source A related resource from which the described resource is derived Communications in Computer and Information Science, Vol-1483, pp. 380-390. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Date A point or period of time associated with an event in the lifecycle of the resource 2021-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-030-91244-4_30" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-030-91244-4_30</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122516327&amp;doi=10.1007%2F978-3-030-91244-4_30&amp;partnerID=40&amp;md5=be50908b7b2c024fc662217e06cb21a2" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122516327&amp;doi=10.1007%2f978-3-030-91244-4_30&amp;partnerID=40&amp;md5=be50908b7b2c024fc662217e06cb21a2</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 18650929; ISBN: 978-303091243-7 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 Sequeira S., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India; Banu P.K.N., Department of Computer Science, CHRIST (Deemed to be University), Bangalore, India