Predictive Analysis of Cryptocurrency Price Using Deep Learning - Yao, Yecheng; Yi, Jungho; Zhai, Shengjun; Lin, Yuwen; Kim, Taekseung; Zhang, Guihongxuan; Yoonjae Lee, Leonard (International Journal of Engineering and Technology, 2018)
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.