Regression based Analysis for Bitcoin Price Prediction

Authors

  • Azim Muhammad Fahmi
  • Noor Azah Samsudin
  • Aida Mustapha
  • Nazim Razali
  • Shamsul Kamal Ahmad Khalid

DOI:

https://doi.org/10.14419/ijet.v7i4.38.27642

Published:

2018-12-03

Keywords:

Bitcoin, Cryptocurrency, Price prediction, Data mining.

Abstract

In 2017, a significant number of individuals profited from the staggering growth of the price of Bitcoin from $800 USD in January to almost $20,000 USD in December. Because the cryptocurrency market being relatively new when compared to traditional markets such as stocks, foreign exchange, and gold, there is a significant lack of studies in regard to predicting its price behavior. This research is interested in evaluating a number of regression-based algorithms in predicting the price of the Bitcoin (BTC) against United States Dollar (USD). Among the algorithms that will be investigated include the Linear Regression (LR), Neural Network Regression (NNR), Bayesian Linear Regression (BLR), and Boosted Decision Tree Regression (BDTR). By applying such regression-based analysis algorithms, the findings f should further help document the behavior of such a brand new, challenging yet extremely lucrative market.

 

 

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How to Cite

Muhammad Fahmi, A., Azah Samsudin, N., Mustapha, A., Razali, N., & Kamal Ahmad Khalid, S. (2018). Regression based Analysis for Bitcoin Price Prediction. International Journal of Engineering & Technology, 7(4.38), 1070–1073. https://doi.org/10.14419/ijet.v7i4.38.27642
Received 2019-02-20
Accepted 2019-02-20
Published 2018-12-03