Open Access
E3S Web Conf.
Volume 214, 2020
2020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
Article Number 02040
Number of page(s) 4
Section Machine Learning and Energy Industry Structure Forecast Analysis
Published online 07 December 2020
  1. Mehta Anukrati. “A Comprehensive Guide to Types of Neural Networks. ” 25 Jan. 2019, www.digitalvidya. com/blog/types-of-neuralnetworks/. [Google Scholar]
  2. Hegazy Osman., Omar S.Soliman., and Mustafa Abdul Salam. “A machine learning model for stock market prediction. ” arXiv preprint arXiv:1402.7351 (2014). [Google Scholar]
  3. S. Schulenburg and P. Ross, “Explorations in LCS models of stock trading, ” Advances in Learning Classifier Systems, 2001, pp. 151-180. [Google Scholar]
  4. R.K. Wolfe, “Turning point identification and Bayesian forecasting of a volatile time series, ” Computers and Industrial Engineering, 1988, pp 378-386. [Google Scholar]
  5. Altay Erdinc., and M. Hakan Satman. “Stock market forecasting: artificial neural network and linear regression comparison in an emerging market. ” Journal of Financial Management & Analysis 18. 2 (2005): 18. [Google Scholar]
  6. Choudhry Rohit., and Kumkum Garg. “A hybrid machine learning system for stock market forecasting. ” World Academy of Science, Engineering and Technology 39. 3 (2008): 315-318. [Google Scholar]
  7. Lee, Ming-Chi. “Using support vector machine with a hybrid feature selection method to the stock trend prediction. ” Expert Systems with Applications 36. 8 (2009): 10896-10904. [Google Scholar]
  8. PRML: Bishop, Christopher M. Pattern recognition and machine learning. Springer, 2006. [Google Scholar]
  9. Nielsen, Michael A. Neural networks and deep learning. Vol. 25. San Francisco, CA, USA: Determination press, 2015. [Google Scholar]
  10. Shah, Vatsal H. “Machine learning techniques for stock prediction. ” Foundations of Machine Learning| Spring 1. 1 (2007): 6-12. [Google Scholar]

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