A Study on Hybrid Deep Learning Approaches for “Monero” Cryptocurrency Price Prediction

Abstract

Traders and investors are always looking for a way to predict the price of cryptocurrencies to increase their returns and reduce their risks. However, due to unpredictability, instability, and movement, cryptocurrency price prediction is a challenging task. Researchers have proposed different architectures for prediction based on statistical approaches, machine learning (ML), and deep learning (DL) techniques. In this article, we aim to evaluate some of these approaches by implementing their proposed architectures on historical data of Monero (XMR) cryptocurrency from the beginning of 2016 to the end of November 2023 and compare the results. According to the obtained results, the CNN-LSTM-Dense architecture performs better based on the Mean Squared Error (MSE) evaluation metric by achieving a value of 0.00472.