Improving the Cryptocurrency Price Prediction Performance Based on Reinforcement Learning

被引：21

作者：

Shahbazi, Zeinab ^{[1
]}

Byun, Yung-Cheol ^{[1
]}

机构：

[1] Jeju Natl Univ, Dept Comp Engn, Major Elect Engn, Jeju 63243, South Korea

来源：

IEEE ACCESS | 2021年 / 9卷

关键词：

Blockchains; Bitcoin; Long short term memory; Market research; Reinforcement learning; Predictive models; Internet; Cryptocurrency; price prediction; machine learning; reinforcement learning;

D O I：

10.1109/ACCESS.2021.3133937

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

During recent developments, cryptocurrency has become a famous key factor in financial and business opportunities. However, the cryptocurrency investment is not visible regarding the market's inconsistent aspect and volatility of high prices. Due to the real-time prediction of prices, the previous approaches in price prediction doesn't contain enough information and solution for forecasting the price changes. Based on the mentioned problems in cryptocurrency price prediction, we proposed a machine learning-based approach to price prediction for a financial institution. The proposed system contains the blockchain framework for secure transaction environment and Reinforcement Learning algorithm for analysis and prediction of price. The main focus of this system is on Litecoin and Monero cryptocurrencies. The results show the presented system accurate the performance of price prediction higher than another state-of-art algorithm.

引用

页码：162651 / 162659

页数：9

共 20 条

[1] Anticipating Cryptocurrency Prices Using Machine Learning [J].