학술논문

Cryptocurrency Analysis using Machine Learning and Deep Learning
Document Type
Conference
Source
2021 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) Medicine and Biology Symposium (SPMB), 2021 IEEE Signal Processing in. :01-03 Dec, 2021
Subject
Bioengineering
Signal Processing and Analysis
Deep learning
Banking
Signal processing
Big Data
Biology
Peer-to-peer computing
Cryptography
Language
ISSN
2473-716X
Abstract
Unlike typical banking transactions, blockchain-assisted cryptocurrencies are touted as the currency of the future, allowing peer-to-peer transactions without the need for an intermediary [1]. According to investors, the crypto share market has grown significantly in terms of market capacity, increasing by 300 percent in a year to approximately 1.6 trillion dollars [2]. Crypto investments, on the other hand, are thought to be dangerous given the crypto market's extremely volatile, latent, and non-stationary nature [3]. Stakeholders and investors may be able to easily incorporate crypto into their investment strategy if they can accurately predict the temporal change of the market price over time. In order to anticipate future prices, machine learning (ML) and big data analytics are extremely effective in deciphering stochastic and nonlinear patterns within market data [4].