Open Access
Issue
E3S Web Conf.
Volume 218, 2020
2020 International Symposium on Energy, Environmental Science and Engineering (ISEESE 2020)
Article Number 01050
Number of page(s) 5
Section Research on Energy Technology Application and Consumption Structure
DOI https://doi.org/10.1051/e3sconf/202021801050
Published online 11 December 2020
  1. B.M. Henrique, V.A. Sobreiro, H. Kimura, Literature review: Machine learning techniques applied to financial market prediction, Expert Systems with Applications, 124 (2019), pp. 226-251. [CrossRef] [Google Scholar]
  2. S. McNally, J. Roche and S. Caton, “Predicting the Price of Bitcoin Using Machine Learning, ” 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), Cambridge, 2018, pp. 339-343. [CrossRef] [Google Scholar]
  3. E. Weiss, “Forecasting commodity prices using ARIMA”, vol. 18, no. 1, 2000, pp. 18-19. [Google Scholar]
  4. J. Contreras, R. Espinola, F. J. Nogales and A. J. Conejo, “ARIMA models to predict next-day electricity prices, ” in IEEE Transactions on Power Systems, vol. 18, no. 3, 2003, pp. 1014-1020 [CrossRef] [Google Scholar]
  5. T. Phaladisailoed and T. Numnonda, “Machine Learning Models Comparison for Bitcoin Price Prediction, ” 2018 10th International Conference on Information Technology and Electrical Engineering (ICITEE), Kuta, 2018, pp. 506-511. [Google Scholar]

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