Prediction of Cryptocurrency Price Trend Using Gradient Boosting


KIPS Transactions on Software and Data Engineering, Vol. 7, No. 10, pp. 387-396, Oct. 2018
10.3745/KTSDE.2018.7.10.387, Full Text:
Keywords: Price Prediction, Cryptocurrency, Machine Learning, Supervised Learning, Gradient Boosting
Abstract

Stock price prediction has been a difficult problem to solve. There have been many studies to predict stock price scientifically, but it is still impossible to predict the exact price. Recently, a variety of types of cryptocurrency has been developed, beginning with Bitcoin, which is technically implemented as the concept of distributed ledger. Various approaches have been attempted to predict the price of cryptocurrency. Especially, it is various from attempts to stock prediction techniques in traditional stock market, to attempts to apply deep learning and reinforcement learning. Since the market for cryptocurrency has many new features that are not present in the existing traditional stock market, there is a growing demand for new analytical techniques suitable for the cryptocurrency market. In this study, we first collect and process seven cryptocurrency price data through Bithumb's API. Then, we use the gradient boosting model, which is a data-driven learning based machine learning model, and let the model learn the price data change of cryptocurrency. We also find the most optimal model parameters in the verification step, and finally evaluate the prediction performance of the cryptocurrency price trends.


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Cite this article
[IEEE Style]
J. Heo, D. Kwon, J. Kim, Y. Han and C. An, "Prediction of Cryptocurrency Price Trend Using Gradient Boosting," KIPS Transactions on Software and Data Engineering, vol. 7, no. 10, pp. 387-396, 2018. DOI: 10.3745/KTSDE.2018.7.10.387.

[ACM Style]
Joo-Seong Heo, Do-Hyung Kwon, Ju-Bong Kim, Youn-Hee Han, and Chae-Hun An. 2018. Prediction of Cryptocurrency Price Trend Using Gradient Boosting. KIPS Transactions on Software and Data Engineering, 7, 10, (2018), 387-396. DOI: 10.3745/KTSDE.2018.7.10.387.