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Complexity
Volume 2018, Article ID 8983590, 16 pages
https://doi.org/10.1155/2018/8983590
Research Article

Anticipating Cryptocurrency Prices Using Machine Learning

1Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
2City, University of London, Department of Mathematics, London EC1V 0HB, UK
3Nokia Bell Labs, Cambridge CB3 0FA, UK
4UCL Centre for Blockchain Technologies, University College London, UK

Correspondence should be addressed to Andrea Baronchelli; ku.ca.ytic@1.illehcnorab.aerdna

Received 29 May 2018; Revised 28 September 2018; Accepted 17 October 2018; Published 4 November 2018

Academic Editor: Massimiliano Zanin

Copyright © 2018 Laura Alessandretti et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [9 citations]

The following is the list of published articles that have cited the current article.

  • Ismael Estalayo, Javier Del Ser, Eneko Osaba, Miren Nekane Bilbao, Khan Muhammad, Akemi Galvez, and Andres Iglesias, “Return, Diversification and Risk in Cryptocurrency Portfolios using Deep Recurrent Neural Networks and Multi-Objective Evolutionary Algorithms,” 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 755–761, . View at Publisher · View at Google Scholar
  • Abeer ElBahrawy, Laura Alessandretti, and Andrea Baronchelli, “Wikipedia and Digital Currencies: Interplay Between Collective Attention and Market Performance,” SSRN Electronic Journal, . View at Publisher · View at Google Scholar
  • Suhwan Ji, Jongmin Kim, and Hyeonseung Im, “A Comparative Study of Bitcoin Price Prediction Using Deep Learning,” Mathematics, vol. 7, no. 10, pp. 898, 2019. View at Publisher · View at Google Scholar
  • Tianyu Ray Li, Anup S. Chamrajnagar, Xander R. Fong, Nicholas R. Rizik, and Feng Fu, “Sentiment-Based Prediction of Alternative Cryptocurrency Price Fluctuations Using Gradient Boosting Tree Model,” Frontiers in Physics, vol. 7, 2019. View at Publisher · View at Google Scholar
  • Alejandro Baldominos, and Yago Saez, “Coin.AI: A Proof-of-Useful-Work Scheme for Blockchain-Based Distributed Deep Learning,” Entropy, vol. 21, no. 8, pp. 723, 2019. View at Publisher · View at Google Scholar
  • Giorgio Lucarelli, and Matteo Borrotti, “A Deep Reinforcement Learning Approach for Automated Cryptocurrency Trading,” Artificial Intelligence Applications and Innovations, vol. 559, pp. 247–258, 2019. View at Publisher · View at Google Scholar
  • Higor Y. D. Sigaki, Matjaž Perc, and Haroldo V. Ribeiro, “Clustering patterns in efficiency and the coming-of-age of the cryptocurrency market,” Scientific Reports, vol. 9, no. 1, 2019. View at Publisher · View at Google Scholar
  • Vladimir N. Soloviev, and Andriy Belinskiy, “Complex Systems Theory and Crashes of Cryptocurrency Market,” Information and Communication Technologies in Education, Research, and Industrial Applications, vol. 1007, pp. 276–297, 2019. View at Publisher · View at Google Scholar
  • Shalin Hai-Jew, “Buy/Hold/Trade or Sell/Divest/Disengage,” Electronic Hive Minds on Social Media, pp. 121–202, 2019. View at Publisher · View at Google Scholar