πŸ”¬
Minerva AI (EN)
  • πŸ‘‹Welcome to Minerva AI
  • STUDIES & FINDINGS
    • ☝️Abstract
    • πŸ“Introduction and related works
    • πŸ’‘Model overview
      • ♾️Convolutional neural network - CNN
      • ✍️Dropout techniques
      • 🧠Long short-term memory (LSTM)
      • πŸ™†Methodology
      • πŸ”—Data collection and merging
      • πŸ› οΈModel architecture
      • βž•Dataset and Parameter Optimization
      • πŸ‘ŒResults and discussion
      • βš“Trading Philosophy & Method
      • βž—Basic algorithm
      • 🌠Results
    • πŸ‘‰Conclusion
  • PRODUCT DEVELOPMENT
    • πŸ‘€Vision and development roadmap
    • 🌟Revenue models
    • πŸͺ™Tokenomics
  • TEAM
    • πŸ‘₯Founding team
  • RESOURCES
    • πŸ“—References
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References

PreviousFounding team

Last updated 1 year ago

  1. Nakamoto, S. Bitcoin: A peer-to-peer electronic cash system. In Decentralized Business Review; Seoul, Korea, 2008; p. 21260. Available online: (accessed on 19 June 2022)

  2. Livieris, I.E.; Kiriakidou, N.; Stavroyiannis, S.; Pintelas, P. An advanced CNN-LSTM model for cryptocurrency forecasting. Electronics 2021, 10, 287. [CrossRef]

  3. Hoseinzade E, Haratizadeh S (2019) CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Syst Appl 129:273–285.

  4. Kara Y, AcarBoyacioglu M, Baykan Γ–K (2011) Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Istanbul stock exchange. Expert Syst Appl 38(5):5311–5319.

  5. Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444.

  6. Nelson DMQ, Pereira ACM, De Oliveira RA (2010) 2011 international joint conference on neural networks. IEEE Trans Neural Netw 21(8):1378–1378.

  7. Di Persio L, Honchar O (2016) Artificial neural networks architectures for stock price prediction: comparisons and applications. International Journal of Circuits, Systems and Signal Processing 10:403–413

  8. Gunduz H, Yaslan Y, Cataltepe Z (2017) Intraday prediction of borsa Istanbul using convolutional neural networks and feature correlations. Knowl-Based Syst 137:138–148

  9. ArΓ©valo R, GarcΓ­a J, Guijarro F, Peris A (2017) A dynamic trading rule based on filtered flag pattern recognition for stock market price forecasting. Expert Systems with Applications 81:177–92

  10. Gunduz H, Yaslan Y, Cataltepe Z (2017) Intraday prediction of borsa Istanbul using convolutional neural networks and feature correlations. Knowl-Based Syst 137:138–148

  11. Nelson DMQ, Pereira ACM, De Oliveira RA (2010) 2011 international joint conference on neural networks. IEEE Trans Neural Netw 21(8):1378–1378.

  12. Hoseinzade E, Haratizadeh S (2019) CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Syst Appl 129:273–285.

πŸ“—
https://www.debr.io/article/21260-bitcoin-a-peer-to-peer-electronic-cash-system
https://doi.org/10.1016/j.eswa.2019.03.029
https://doi.org/10.1016/j.eswa.2010.10.027
https://doi.org/10.1038/nature14539
https://doi.org/10.1109/tnn.2010.2063350
https://doi.org/10.1109/tnn.2010.2063350
https://doi.org/10.1016/j.eswa.2019.03.029