Open AccessPredictive Analysis of Cryptocurrency Price Using Deep Learning
Author(s) -
Yecheng Yao,
Jungho Yi,
Shengjun Zhai,
YuWen Lin,
Taek-Seung Kim,
Guihongxuan Zhang,
Leonard Yoonjae Lee
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.27.17889
Subject(s) - cryptocurrency , computer science , python (programming language) , benchmark (surveying) , artificial intelligence , deep learning , machine learning , econometrics , artificial neural network , economics , computer security , geodesy , geography , operating system
The decentralization of cryptocurrencies has greatly reduced the level of central control over them, impacting international relations and trade. Further, wide fluctuations in cryptocurrency price indicate an urgent need for an accurate way to forecast this price. This paper proposes a novel method to predict cryptocurrency price by considering various factors such as market cap, volume, circulating supply, and maximum supply based on deep learning techniques such as the recurrent neural network (RNN) and the long short-term memory (LSTM),which are effective learning models for training data, with the LSTM being better at recognizing longer-term associations. The proposed approach is implemented in Python and validated for benchmark datasets. The results verify the applicability of the proposed approach for the accurate prediction of cryptocurrency price.