GRU Prediction Method For Digital Cryptocurrency Prices

Abstract

With the rapid development of the Internet, digital cryptocurrencies based on blockchain technology have been widely used globally. However, the huge volatility and high risk of cryptocurrency prices pose challenges for investors. To address this issue, predicting the prices of digital cryptocurrencies has become a research focus. However, most existing studies mainly focus on Bitcoin price prediction. This paper proposes a GRU (Gated Recurrent Unit) model-based method for predicting the price of Dogecoin, a popular emerging cryptocurrency. The choice of Dogecoin is motivated by its high price volatility and prediction difficulty as a relatively new cryptocurrency. The GRU model is a variant of the recurrent neural network (RNN) that has better prediction performance compared to the LSTM model. With this method, we can effectively predict the price of Dogecoin, reducing investment risks for investors and providing reference for policymakers in regulating the digital currency market.