There has been much interest in accurate cryptocurrency price forecast models by investors and researchers. Deep Learning models are prominent machine learning techniques that have transformed various fields and have shown potential for finance and economics. Although various deep learning models have been explored for cryptocurrency price forecasting, it is not clear which models are suitable due to high market volatility. In this study, we review the literature about deep learning for cryptocurrency price forecasting and evaluate novel deep learning models for cryptocurrency stock price prediction. Our deep learning models include variants of long short-term memory (LSTM) recurrent neural networks, variants of convolutional neural networks (CNNs), and the Transformer model. We evaluate univariate and multivariate approaches for multi-step ahead predicting of cryptocurrencies close-price. We also carry out volatility analysis on the four cryptocurrencies which reveals significant fluctuations in their prices throughout the COVID-19 pandemic. Additionally, we investigate the prediction accuracy of two scenarios identified by different training sets for the models. First, we use the pre-COVID-19 datasets to model cryptocurrency close-price forecasting during the early period of COVID-19. Secondly, we utilise data from the COVID-19 period to predict prices for 2023 to 2024. Our results show that the convolutional LSTM with a multivariate approach provides the best prediction accuracy in two major experimental settings. Our results also indicate that the multivariate deep learning models exhibit better performance in forecasting four different cryptocurrencies when compared to the univariate models.

翻译：投资者和研究者对精确的加密货币价格预测模型一直抱有浓厚兴趣。深度学习模型作为重要的机器学习技术，已变革多个领域，并在金融经济学中展现出潜力。尽管已有多种深度学习模型被探索用于加密货币价格预测，但由于市场的高波动性，尚不清楚哪些模型最为适用。本研究综述了加密货币价格预测领域的深度学习文献，并评估了用于加密货币股价预测的新型深度学习模型。我们的深度学习模型包括长短期记忆（LSTM）循环神经网络的变体、卷积神经网络（CNN）的变体以及Transformer模型。我们评估了单变量与多变量方法在加密货币收盘价多步超前预测中的表现。同时，我们对四种加密货币进行了波动性分析，揭示了其在COVID-19疫情期间价格的显著波动。此外，我们研究了基于不同训练集划分的两种场景下模型的预测精度：首先，使用疫情前数据集建模以预测COVID-19初期的加密货币收盘价；其次，利用疫情期间数据预测2023至2024年的价格。结果表明，采用多变量方法的卷积LSTM模型在两种主要实验设置下均取得了最佳预测精度。研究还发现，与单变量模型相比，多变量深度学习模型在预测四种不同加密货币时表现出更优的性能。