Enhancing Bitcoin Price Fluctuation Prediction Using Attentive LSTM and Embedding Network

Applied Sciences

Hong-Ning Dai¹  Yang Li²  Zibin Zheng² 

[1] Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China;School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510275, China;

关键词: Bitcoin;    price fluctuation prediction;    deep learning;    embedding network;    long short term memory;   

DOI  :  10.3390/app10144872

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown