Toward Stock Price Prediction using Deep Learning

Chun Hung Cho, Guan Yi Lee, Yueh Lin Tsai, Kun Chan Lan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Three methods including LSTM, Seq2seq and WaveNet are implemented in this study. We compare the performance of different deep learning methods in predicting stock prices. We use the correlation between the predicted price and the actual price as the performance metric to evaluate the effectiveness of these methods.

Original languageEnglish
Title of host publicationUCC 2019 Companion - Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing
PublisherAssociation for Computing Machinery, Inc
Pages133-135
Number of pages3
ISBN (Electronic)9781450370448
DOIs
Publication statusPublished - 2019 Dec 2
Event12th IEEE/ACM International Conference on Utility and Cloud Computing, UCC Companion 2019 - Auckland, New Zealand
Duration: 2019 Dec 22019 Dec 5

Publication series

NameUCC 2019 Companion - Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing

Conference

Conference12th IEEE/ACM International Conference on Utility and Cloud Computing, UCC Companion 2019
CountryNew Zealand
CityAuckland
Period19-12-0219-12-05

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All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture

Cite this

Cho, C. H., Lee, G. Y., Tsai, Y. L., & Lan, K. C. (2019). Toward Stock Price Prediction using Deep Learning. In UCC 2019 Companion - Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing (pp. 133-135). (UCC 2019 Companion - Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing). Association for Computing Machinery, Inc. https://doi.org/10.1145/3368235.3369367