Title: Long‐and‐Short‐Term Memory (LSTM) NetworksArchitectures and Applications in Stock Price Prediction
Abstract: Chapter 8 Long-and-Short-Term Memory (LSTM) NetworksArchitectures and Applications in Stock Price Prediction Jaydip Sen, Jaydip SenSearch for more papers by this authorSidra Mehtab, Sidra MehtabSearch for more papers by this author Jaydip Sen, Jaydip SenSearch for more papers by this authorSidra Mehtab, Sidra MehtabSearch for more papers by this author Book Editor(s):Umang Singh, Umang Singh ITS, Ghaziabad (U.P.), India (deceased), Institute of Semiconductors, Chinese Academy of Sciences, Beijing, ChinaSearch for more papers by this authorSan Murugesan, San Murugesan BRITE Professional Service, Sydney, AustraliaSearch for more papers by this authorAshish Seth, Ashish Seth Inha University, Incheon, South KoreaSearch for more papers by this author First published: 11 July 2022 https://doi.org/10.1002/9781119813439.ch8 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat Abstract Although recurrent neural networks (RNNs) are effective in handling sequential data, they are poor in capturing the long-term dependencies in the data due to a problem known as vanishing and exploding gradients. A variant of RNNs known as Long-and-Short-Term Memory (LSTM) networks effectively gets rid of the problem, and hence these networks are proved to be very efficient and accurate in handling sequential data. This chapter presents the basic design of LSTM networks and highlights their working principles. Six different variants of LSTM models are also presented with a particular focus on stock price forecasting. The models are trained and tested on the historical NIFTY 50 index of the National Stock Exchange (NSE) of India from December 29, 2014 to July 31, 2020. The performances of the models are compared on the basis of their execution speeds and prediction accuracies. It is observed that while the univariate LSTMs with the basic architecture are more accurate than their encoder-decoder counterparts, the opposite is the case for the execution speed. Emerging Computing Paradigms: Principles, Advances and Applications RelatedInformation
Publication Year: 2022
Publication Date: 2022-07-11
Language: en
Type: other
Indexed In: ['crossref']
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Cited By Count: 16
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