Stock Prediction with Random Forests and Long Short-term Memory Han, Shangxuan
dc.contributor.department Electrical and Computer Engineering
dc.contributor.majorProfessor Zambreno, Joseph 2020-01-07T20:10:33.000 2020-06-30T01:34:42Z 2020-06-30T01:34:42Z Tue Jan 01 00:00:00 UTC 2019 2019-01-01
dc.description.abstract <p>Machine learning as a popular computer science area has been promoted and developed for more than two decades. It has been applied in many fields in our life, like domestic products such as Alexa from Amazon, photographic products such as Mavic from Dji and so many other areas. This report represents an interesting way to apply machine learning and deep learning technologies on the stock market. We explore multiple approaches, including Long Short-Term Memory (LSTM), a type of Artificial Recurrent Neural Networks (RNN) architectures, and Random Forests (RF), a type of ensemble learning methods. The goal of this report is to use real historical data from the stock market to train our models, and to show reports about the prediction of future returns for picked stocks.</p>
dc.format.mimetype PDF
dc.identifier archive/
dc.identifier.articleid 1437
dc.identifier.contextkey 15854239
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath creativecomponents/393
dc.source.bitstream archive/|||Fri Jan 14 23:55:54 UTC 2022
dc.subject.disciplines Computer Engineering
dc.subject.keywords machine learning
dc.subject.keywords deep learning
dc.subject.keywords artificial neural network
dc.subject.keywords long short-term memory
dc.subject.keywords random forests
dc.subject.keywords ensemble learning.
dc.title Stock Prediction with Random Forests and Long Short-term Memory
dc.type article
dc.type.genre creativecomponent
dspace.entity.type Publication
relation.isOrgUnitOfPublication a75a044c-d11e-44cd-af4f-dab1d83339ff Computer Engineering creativecomponent
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