Bitcoin price prediction based on recurrent neural network

Yuxuan Ren

doi:10.1117/12.2655903

12 January 2023 Bitcoin price prediction based on recurrent neural network

Yuxuan Ren

Proceedings Volume 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022); 1250927 (2023) https://doi.org/10.1117/12.2655903
Event: Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 2022, Guangzhou, China

Abstract

Bitcoin is a currency with high price volatility, and it is difficult to predict the Bitcoin price accurately. In recent years, many optimization strategies based on neural networks have been proposed to improve the performance of prediction tasks. The most common ones are extending the dataset, adding extra features, and adjusting output length. This paper aims to verify whether these strategies work on the Bitcoin prediction problem. Three relevant experiments are proposed to train gated recurrent unit models, one of the recurrent neural network models. The evaluation scores are calculated using mean absolute percentage error, root mean square error, and the coefficient of determination. The results show that none of the above three methods can improve the model performance on a four-year daily trading dataset, which proves that the Bitcoin market has its characteristics. In addition, the results demonstrate that R-squared is necessary for evaluating the result of the regression problems. Finally, this paper suggests designing an evaluation function for the Bitcoin price prediction problem to meet the prediction purpose better: reduce investment risks.

Citation Download Citation

Yuxuan Ren "Bitcoin price prediction based on recurrent neural network", Proc. SPIE 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 1250927 (12 January 2023); https://doi.org/10.1117/12.2655903

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available