Time Series Analysis of Cryptocurrency Prices Using Long Short-Term Memory
Jacques Fleischer, Gregor von Laszewski, Carlos Theran, Yohn Jairo Parra Bautista
Abstract
Digitization is changing our world, creating innovative finance channels and emerging technology such as cryptocurrencies, which are applications of blockchain technology. However, cryptocurrency price volatility is one of this technology’s main trade-offs. In this paper, we explore a time series analysis using deep learning to study the volatility and to understand this behavior. We apply a long short-term memory model to learn the patterns within cryptocurrency close prices and to predict future prices. The proposed model learns from the close values. The performance of this model is evaluated using the root-mean-squared error and by comparing it to an ARIMA model.