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IJML 2024 Vol.14(1): 7-11
DOI: 10.18178/ijml.2024.14.1.1150

Augmented Machine Intelligence with Human Intelligence for Cryptocurrency Price Prediction

Melika Honarmand1,*, Arushee Saxena2, Sahaj Shandilya3, Abhishek Chaudhary3, Nikhil Meena3, and Philip Treleaven2
1. Amirkabir University of Technology, Tehran Polytechnic, Iran
2. University College London, London, UK
3. Indian Institute of Technology, Kanpur, India
Email: melikahonarmand@aut.ac.ir (M.H.); arusheesaxena@gmail.com (A.S.); sahajs21@iitk.ac.in (S.S.); abhishekc21@iitk.ac.in (A.C.); nmeena21@iitk.ac.in (N.M.); p.treleaven@ucl.ac.uk (P.T.)
*Corresponding author

Manuscript received May 2, 2023; revised June 1, 2023; accepted July 4, 2023; published January 26, 2024

Abstract—This research explores the integration of human and machine intelligence in the financial industry. While AI systems excel at analyzing data, humans possess unique traits that contribute to accurate predictions. Inspired by the concept of Augmented Financial Intelligence, the study aims to integrate human intelligence with existing models, considering the limitations of current human input methods. Natural Language Processing and Sentiment Analysis are used to enhance prediction tasks, but limited training data and suboptimal processing can introduce noise. The study introduces a framework that integrates human and machine intelligence to enhance cryptocurrency market forecasts, analyzing six cryptocurrencies and utilizing an Elo-based rating system to identify exceptional predictors. The framework aims to minimize noise and optimize human input in financial forecasting.

Keywords—augmented intelligence, cryptocurrency, elo rating, super forecasting


Cite: Melika Honarmand, Arushee Saxena, Sahaj Shandilya, Abhishek Chaudhary, Nikhil Meena, and Philip Treleaven, "Augmented Machine Intelligence with Human Intelligence for Cryptocurrency Price Prediction," International Journal of Machine Learning vol. 14, no. 1, pp. 7-11, 2024.

Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net

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