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This study applies Long Short-Term Memory (LSTM), a recurrent neural network designed for time series forecasting, to model Bitcoin and Ethereum values using historical price data from Yahoo Finance. Data are split into 80% training and 20% testing. The work builds LSTM models for each dataset and compares them with gated recurrent unit (GRU). Evaluation uses RMSE, MAE, and MAPE.",{"@graph":63,"@context":119},[64,80,102],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/predicting-bitcoin-and-ethereum-prices-using-long-short-term-memory-and-gated-recurrent-unit/569633/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/predicting-bitcoin-and-ethereum-prices-using-long-short-term-memory-and-gated-recurrent-unit/569633.png","ImageObject",442,249,{"name":88,"@type":89},"noah","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-21","2026-10-13",true,{"@type":98,"interactionType":99,"userInteractionCount":101},"InteractionCounter",{"@type":100},"ViewAction",6,{"@type":103,"mainEntity":104},"FAQPage",[105,111,115],{"name":106,"@type":107,"acceptedAnswer":108},"Why is predicting Bitcoin and Ethereum prices challenging?","Question",{"text":109,"@type":110},"Because cryptocurrency prices are inherently volatile, making future price movements difficult to forecast reliably.","Answer",{"name":112,"@type":107,"acceptedAnswer":113},"What models are used to predict Bitcoin and Ethereum in this study?",{"text":114,"@type":110},"The study uses Long Short-Term Memory (LSTM) for time series forecasting and also explores the gated recurrent unit (GRU) for comparison.",{"name":116,"@type":107,"acceptedAnswer":117},"How is model performance evaluated?",{"text":118,"@type":110},"Performance is assessed using RMSE, MAE, and MAPE, computed on a 20% testing partition after training on 80% of the data.","https://schema.org",{"og:url":78,"og:type":121,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":123,"canonical":78},"index,follow",{"doc_id":125,"site_id":56},569633,1792094052,{"code":4,"msg":5,"data":128},{"doc_id":125,"user_id":129,"nickname":88,"user_avatar":130,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":131,"file_id":132,"file_url":133,"file_type":134,"file_size":135,"view_count":101,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":136,"language":137,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":138,"faqs":139,"seo_title":140,"seo_description":61,"update_tm":141,"read_time":79},1099529839121,"https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d","Predicting Bitcoin and Ethereum prices using long short-term memory and gated recurrent unit  \nM. H. Abdul Hadi1 , N.A. Ramli1*, and Q. U. I Islam2  \n1Centre for Mathematical Sciences, Universiti Malaysia Pahang Al-Sultan Abdullah, Lebuhraya Persiaran Tun Khalil Yaakob, 26300 Kuantan, Pahang, Malaysia  \n2Department of Electrical and Computer Engineering, College of Engineering, Dhofar University, Salalah, Sultanate of Oman  \nABSTRACT - Predicting future prices of cryptocurrencies, including Bitcoin and Ethereum, presents a formidable challenge owing to their inherent volatility. This study applies Long Short-Term Memory (LSTM), a well-established recurrent neural network for time series forecasting, to predict Bitcoin and Ethereum values. Historical price data for both cryptocurrencies, sourced from Yahoo Finance, serves as the basis for analysis. The dataset undergoes an 80% training and 20% testing partition. Subsequently, LSTM models are developed and trained on both datasets. In parallel, the gated recurrent unit (GRU), recognized as an advanced variant of the LSTM model, is explored for comparative purposes. Performance evaluation utilizes fundamental metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) . The results reveal an intriguing trend: both models exhibit superior performance when applied to the Ethereum dataset compared to the Bitcoin dataset. This observation suggests the potential presence of Ethereum-specific features or patterns that align more effectively with deep learning model architectures. Notably, the GRU model consistently outperforms the LSTM model across RMSE, MAE, and MAPE. These outcomes underscore the GRU model’s capacity as a robust tool for cryptocurrency value prediction. In summary, this study tackles the challenge of cryptocurrency price prediction while emphasizing the promising role of advanced neural network architectures, such as GRU, in enhancing prediction accuracy, thus offering valuable insights into financial forecasting.  \nARTICLE HISTORY  \nReceived : 27/01/2023  \nRevised : 17/02/2023  \nAccepted : 29/05/2023  \nPublished : 30/09/2023  \nKEYWORDS  \nCryptocurrency Bitcoin Ethereum  \nDeep learning Predictive modelling  \n1. INTRODUCTION  \nCryptocurrency is a digital currency that people can buy or sell, and all these activities can be done directly without permission from the bank. One example of cryptocurrency is Bitcoin, created by Satoshi Nakamoto in 2009 and shortened to BTC. Bitcoin is an electronic payment system based on cryptography. Since it was launched in 2009, Bitcoin has risen in value. As of today (26th February 2024), the Bitcoin price in Malaysia is RM244,280.02 per one Bitcoin. Any other cryptocurrencies were established after the establishment of Bitcoin. Ethereum is the second largest cryptocurrency after Bitcoin based on market value. Ethereum or Ether (ETH) was launched in 2015 and built on Bitcoin innovation. Vilatik Buterin created Ethereum, and it uses peer-to-peer networks as its blockchain. Ethereum’s price is now RM14,624.02, which is equal to one ETH.  \nBitcoin and Ethereum work in the same network, but different cryptocurrencies called blockchain. Blockchain is a technology system network known as Distributed Ledger Technology (DLT) . Simple terms in a blockchain are a series of data records managed by a group of computers not owned by a single entity. Predicting cryptocurrencies such as Bitcoin and Ethereum is challenging because of their volatility. However, several methods have been tested to predict the future price of Bitcoin and Ethereum. One of the most used methods in predicting cryptocurrencies is Long Short-Term Memory (LSTM). LSTM is a modification of the recurrent neural network (RNN) . LSTM is to complete the lack of RNN prediction since it passes information stored over a long period.  \nLSTM can remember the sets of information that have been stored over the long term, thus remo","cbCaib3mnC42NHEq","https://ap.wps.com/l/cbCaib3mnC42NHEq","pdf",729004,10,"English","# Abstract\n# Article History\n# Keywords\n# 1. Introduction\n## Cryptocurrency background and volatility\n## Time-series forecasting with LSTM\n## Related work using LSTM/GRU and other indicators\n## Motivation for comparing LSTM vs GRU","[{\"question\":\"Why is predicting Bitcoin and Ethereum prices challenging?\",\"answer\":\"Because cryptocurrency prices are inherently volatile, making future price movements difficult to forecast reliably.\"},{\"question\":\"What models are used to predict Bitcoin and Ethereum in this study?\",\"answer\":\"The study uses Long Short-Term Memory (LSTM) for time series forecasting and also explores the gated recurrent unit (GRU) for comparison.\"},{\"question\":\"How is model performance evaluated?\",\"answer\":\"Performance is assessed using RMSE, MAE, and MAPE, computed on a 20% testing partition after training on 80% of the data.\"}]","Predicting Bitcoin and Ethereum Prices Using Long Short-Term Memory and Gated Recurrent Unit | PDF",1791876775]