Enhancing Bitcoin Price Fluctuation Prediction Using Attentive LSTM and Embedding Network
School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510275, China
National Engineering Research Center of Digital Life, Sun Yat-sen University, Guangzhou 510275, China
Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(14), 4872; https://doi.org/10.3390/app10144872
Received: 2 June 2020 / Revised: 7 July 2020 / Accepted: 8 July 2020 / Published: 16 July 2020
(This article belongs to the Section Computing and Artificial Intelligence)
Bitcoin has attracted extensive attention from investors, researchers, regulators, and the media. A well-known and unusual feature is that Bitcoin’s price often fluctuates significantly, which has however received less attention. In this paper, we investigate the Bitcoin price fluctuation prediction problem, which can be described as whether Bitcoin price keeps or reversals after a large fluctuation. In this paper, three kinds of features are presented for the price fluctuation prediction, including basic features, traditional technical trading indicators, and features generated by a Denoising autoencoder. We evaluate these features using an Attentive LSTM network and an Embedding Network (ALEN). In particular, an attentive LSTM network can capture the time dependency representation of Bitcoin price and an embedding network can capture the hidden representations from related cryptocurrencies. Experimental results demonstrate that ALEN achieves superior state-of-the-art performance among all baselines. Furthermore, we investigate the impact of parameters on the Bitcoin price fluctuation prediction problem, which can be further used in a real trading environment by investors.