Development of a cryptocurrency price prediction model: leveraging GRU and LSTM for Bitcoin, Litecoin and Ethereum.

IF 3.5 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
PeerJ Computer Science Pub Date : 2025-03-17 eCollection Date: 2025-01-01 DOI:10.7717/peerj-cs.2675
Ramneet Kaur, Mudita Uppal, Deepali Gupta, Sapna Juneja, Syed Yasser Arafat, Junaid Rashid, Jungeun Kim, Roobaea Alroobaea
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引用次数: 0

Abstract

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.

开发加密货币价格预测模型:利用比特币、莱特币和以太坊的GRU和LSTM。
加密货币代表了金融技术进步产生的一种资产形式,为学术研究提供了重要前景。研究人员和投资者非常希望能够极其准确地预测加密货币的价格。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,使精确的价格预测复杂化。一些研究利用各种深度学习(DL)算法探索了加密货币的价格预测。三种主要的加密货币,由市值决定,以太坊(ETH),比特币(BTC)和莱特币(LTC),在本研究中检查汇率预测。本文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三种加密货币的预测准确性,即均方误差(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),这表明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。该模型使用来自cryptodatdownload的规范化历史价格数据进行训练,并采用80:20的列车测试分割。在这项工作中,GRU有资格成为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,表明GRU的预测比LSTM更准确。这些预测模型对交易者和投资者来说很有价值,提供了准确的加密货币价格预测。未来的研究还应考虑其他变量,例如可能影响加密货币定价的社交媒体趋势和交易量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
PeerJ Computer Science
PeerJ Computer Science Computer Science-General Computer Science
CiteScore
6.10
自引率
5.30%
发文量
332
审稿时长
10 weeks
期刊介绍: PeerJ Computer Science is the new open access journal covering all subject areas in computer science, with the backing of a prestigious advisory board and more than 300 academic editors.
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