https://orcid.org/0000-0003-1277-6271
References
- Afia, E., O. A. Abdellatif, and S. Garcia. 2019. Adaptive cooperation of multi-swarm particle swarm optimizer-based hidden Markov model. Progress in Artificial Intelligence 8 (4):101–116. doi:10.1007/s13748-019-00183-1.
- Aoun, O., and A. El Afia. 2014. ‘A Robust crew pairing based on multi-agent Markov decision processes’. In 2014 Second World Conference on Complex Systems (WCCS), 762–68, Agadir, Morocco. https://doi.org/10.1109/ICoCS.2014.7060940.
- Ariyo, A. A., A. Adewumi O., and E. C. K. Ayo. 2014. Stock price prediction using the ARIMA model . In 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, 106–12. IEEE, Cambridge, UK.
- Breiman, L., and R. Ihaka. 1984. Nonlinear discriminant analysis via scaling and ACE. Department of Statistics, University of California.
- ‘DataScienceToday - ML Supervised’. n.d. Accessed 20 December 2020. https://datasciencetoday.net/index.php/fr/machine-learning/109-ml-sup
- Ezzikouri, H., and F. Mohamed. n.d. ‘Algorithmes de classification : ID3 & C4.5ʹ, 36.
- Hochreiter, S. 1998. The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 6(2): 107–116.
- Kendi, S., F. Laib, and M. S. Radjef. 2012. ‘Application des Réseaux de Neurones Récurrents à la Formation de Prix à Terme dans le cas de Deux Traders: Producteur-Consommateur’, 9.
- Lamothe-Fernández, P., D. Alaminos, P. Lamothe-López, and E. M. A. Fernández-Gámez. 2020. Deep learning methods for modeling bitcoin price. Mathematics 8 (8):1245. doi:10.3390/math8081245.
- Luca, D. P., and E. O. Honchar. 2017. Recurrent neural networks approach to the financial forecast of Google assets . International Journal of Mathematics and Computers in Simulation 11:7–13.
- McNally, S., J. Roche, and E. S. Caton. 2018. Predicting the price of bitcoin using machine learning . In 2018 26th euromicro international conference on parallel, distributed and network-based processing (PDP), 339–43. IEEE, Cambridge, UK.
- Mifdal, R. 2019. ‘Application des techniques d’apprentissage automatique pour la prédiction de la tendance des titres financiers’, 196.
- Milosevic, N. 2016. Equity forecast: Predicting long term stock price movement using machine learning . arXiv. preprint arXiv:1603.00751.
- Mostafa, E. L. H. A. C. H. L. O. U. F. I. 2013. ‘Les Apports de l’Intelligence Artificielle Aux Approches Probabilistes Pour l’Optimisation de Portefeuille d’Actifs Financiers’.
- Pudaruth, S. 2014. Predicting the price of used cars using machine learning techniques . Int. J. Inf. Comput. Technol 4 (7):753–64.
- Tadjeddine, Y. 2013. La finance comportementale. Idees Economiques Et Sociales N° 174 (4):16–25.
- Velankar, S., S. Valecha, and E. S. Maji. 2018. Bitcoin price prediction using machine learning . In 2018 20th International Conference on Advanced Communication Technology (ICACT), 144–47. IEEE, Abuja, Nigeria.