数字加密货币
计算机科学
人气
深度学习
圣杯
计量经济学
波动性(金融)
人工智能
经济
机器学习
计算机安全
心理学
社会心理学
万维网
作者
Agis Politis,Katerina Doka,Nectarios Koziris
标识
DOI:10.1109/icbc51069.2021.9461061
摘要
Over the last years, cryptocurrencies have gained popularity as a means of exchange, but mostly as an investment asset that can yield important earnings. Accurate cryptocurrency price prediction is the holy grail of investors, yet the task is extremely complex and tedious since cryptocurrencies exhibit high volatility and steep fluctuations compared to fiat money, while they depend on a plethora of factors related to the blockchain network, market trends, social popularity and the prices of other (crypto)currencies. Thus, simple statistical methods are not able to capture the complexity of cryptocurrency exchange rate, forcing researchers to turn to advanced machine learning techniques. In this work, we present a methodology for building deep learning models to forecast the price of cryptocurrencies and apply it to the prediction of Ether price, resulting in short-and long-term forecasts that achieve an accuracy of up to 84.2%.
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