Kaur Ramneet, Uppal Mudita, Gupta Deepali, Juneja Sapna, Arafat Syed Yasser, Rashid Junaid, Kim Jungeun, Alroobaea Roobaea
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
KIET Group of Institutions, Ghaziabad, India.
PeerJ Comput Sci. 2025 Mar 17;11:e2675. doi: 10.7717/peerj-cs.2675. eCollection 2025.
Cryptocurrency represents a form of asset that has arisen from the progress of financial technology, presenting significant prospects for scholarly investigations. The ability to anticipate cryptocurrency prices with extreme accuracy is very desirable to researchers and investors. However, time-series data presents significant challenges due to the nonlinear nature of the cryptocurrency market, complicating precise price predictions. Several studies have explored cryptocurrency price prediction using various deep learning (DL) algorithms. Three leading cryptocurrencies, determined by market capitalization, Ethereum (ETH), Bitcoin (BTC), and Litecoin (LTC), are examined for exchange rate predictions in this study. Two categories of recurrent neural networks (RNNs), specifically long short-term memory (LSTM) and gated recurrent unit (GRU), are employed. Four performance metrics are selected to evaluate the prediction accuracy namely mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) for three cryptocurrencies which demonstrates that GRU model outperforms LSTM. The GRU model was implemented as a two-layer deep learning network, optimized using the Adam optimizer with a dropout rate of 0.2 to prevent overfitting. The model was trained using normalized historical price data sourced from CryptoDataDownload, with an 80:20 train-test split. In this work, GRU qualifies as the best algorithm for developing a cryptocurrency price prediction model. MAPE values for BTC, LTC and ETH are 0.03540, 0.08703 and 0.04415, respectively, which indicate that GRU offers the most accurate forecasts as compared to LSTM. These prediction models are valuable for traders and investors, offering accurate cryptocurrency price predictions. Future studies should also consider additional variables, such as social media trends and trade volumes that may impact cryptocurrency pricing.
加密货币是金融科技发展产生的一种资产形式，为学术研究提供了重要前景。对于研究人员和投资者来说，能够极其准确地预测加密货币价格是非常理想的。然而，由于加密货币市场的非线性性质，时间序列数据带来了重大挑战，使得精确的价格预测变得复杂。几项研究已经探索了使用各种深度学习（DL）算法进行加密货币价格预测。本研究考察了按市值确定的三种主要加密货币，即以太坊（ETH）、比特币（BTC）和莱特币（LTC）的汇率预测。采用了两类递归神经网络（RNN），即长短期记忆（LSTM）和门控递归单元（GRU）。选择了四个性能指标来评估预测准确性，即三种加密货币的均方误差（MSE）、平均绝对误差（MAE）、平均绝对百分比误差（MAPE）和均方根误差（RMSE），结果表明GRU模型优于LSTM。GRU模型被实现为一个两层深度学习网络，使用Adam优化器进行优化，辍学率为0.2以防止过拟合。该模型使用从CryptoDataDownload获取的归一化历史价格数据进行训练，训练集与测试集的分割比例为80:20。在这项工作中，GRU被认为是开发加密货币价格预测模型的最佳算法。BTC、LTC和ETH的MAPE值分别为0.03540、0.08703和0.04415，这表明与LSTM相比，GRU提供了最准确的预测。这些预测模型对交易者和投资者很有价值，能够提供准确的加密货币价格预测。未来的研究还应考虑其他变量，如可能影响加密货币定价的社交媒体趋势和交易量。
