Open Access
Issue
E3S Web Conf.
Volume 214, 2020
2020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
Article Number 02050
Number of page(s) 6
Section Machine Learning and Energy Industry Structure Forecast Analysis
DOI https://doi.org/10.1051/e3sconf/202021402050
Published online 07 December 2020
  1. K.P. Murphy, Machine learning: a probabilistic perspective, Chance, Vol. 27, no. 2, pp. 62-63, 2012. [Google Scholar]
  2. K.J. Kim, I. Han, Genetic algorithms approach to feature discretization in artificial neural networks for the prediction of stock price index, Expert System Appliance, Vol. 19, no. 2, pp. 125-132, 2000. [Google Scholar]
  3. L. Nan, X. Liang, L. Xin, et al, Network environment and financial risk using machine learning and sentiment analysis, Human and Ecological Risk Assessment: An International Journal, Vol. 15, no. 2, pp. 26, 2009. [Google Scholar]
  4. J. Patel, S. Shah, P. Thakkar, et al, Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques, Expert Systems with Applications An International Journal, Vol. 42, no. 1, pp. 259-268, 2015. [Google Scholar]
  5. S. Yuan, W. Yingnian, Stock Trend Prediction: Based on Machine Learning Methods, UCLA Electronic Theses and Dissertations, 2018. [Google Scholar]
  6. J. Patel, S. Shah, P. Thakkar, et al, Predicting stock market index using fusion of machine learning techniques, Expert Systems with Applications, Vol. 42, no. 4, pp. 2162-2172, 2015. [Google Scholar]
  7. W. Huang, Y. Nakamor, S.Y. Wang, Forecasting stock market movement direction with support vector machine, Computers and Operations Research, Vol. 32, no. 10, pp. 2513-2522, 2005. [Google Scholar]
  8. S. Sohangir, W. Ding, A. Pomeranets, et al, Big data: deep learning for financial sentiment analysis, Journal of Big Data, Vol. 5, no. 1, pp. 3, 2018. [Google Scholar]
  9. M. Abe, H. Nakayama, Deep learning for forecasting stock returns in the cross-section, Pacific Asia Conference on Knowledge Discovery and Data Mining, 2018. [Google Scholar]
  10. E.M. Attua, Using multiple linear regression techniques to quantify carbon stocks of fallow vegetation in the tropics, West African Journal of Applied Ecology, Vol. 12, no. 1, 2009. [Google Scholar]
  11. S. Hochreiter, J. Schmidhuber, Long short-term memory, Neural Computation, Vol. 9, no. 8, pp. 1735-1780, 1997. [CrossRef] [PubMed] [Google Scholar]
  12. J.A.K. Suykens, J. Vandewalle, Least squares support vector machine classifiers, Neural Processing Letters, Vol. 9, no. 3, pp. 293-300, 1999. [CrossRef] [Google Scholar]
  13. V. Svetnik, A. Liaw, et al, Random forest: a classification and regression tool for compound classification and qsar modeling, Journal of Chemical Information and Computer Sciences, Vol. 43, no. 6, pp. 1947, 2003. [Google Scholar]
  14. S.R. Joelsson, J.A. Benediktsson, J.R. Sveinsson, Random forest classifiers for hyperspectral data, IEEE International Geoscience and Remote Sensing Symposium, 2005. [Google Scholar]
  15. P.D. Allison, Logistic Regression Using the SAS System: Theory and Application, SAS Publishing, 1999. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.