[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-569624-105":53,"doc-detail-569624-en":127},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":120,"head_meta":122,"extra_data":124,"updated_unix":126},105,"en","beyond-conventional-methods-advancing-ethereum-price-prediction-through-integrated-technical-on-chain-and-machine-learning-approaches-vol-15-issue-2","Beyond Conventional Methods: Advancing Ethereum Price Prediction through Integrated Technical, On-Chain, and Machine Learning Approaches - Vol 15 - Issue 2","","Ethereum’s anonymity and the uncontrolled attraction of cryptocurrencies draw investor attention to Ethereum’s price dynamics and motivate more reliable prediction methods. The study addresses gaps in prior work that used either technical analysis or on-chain analysis by integrating both. It examines dependent Ethereum price against market and on-chain variables and compares ARIMA, LSTM, and XGBoost, finding strong correlations and higher LSTM prediction accuracy for robust forecasting.",{"@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/beyond-conventional-methods-advancing-ethereum-price-prediction-through-integrated-technical-on-chain-and-machine-learning-approaches-vol-15-issue-2/569624/",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/beyond-conventional-methods-advancing-ethereum-price-prediction-through-integrated-technical-on-chain-and-machine-learning-approaches-vol-15-issue-2/569624.png","ImageObject",442,249,{"name":88,"@type":89},"404brain","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-10-22","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 does the study integrate technical analysis and on-chain analysis for Ethereum price prediction?","Question",{"text":109,"@type":110},"Prior research often focused on either technical indicators or on-chain metrics, which limits the ability to capture synergistic effects. The study combines both to identify relationships and dependencies that support stronger forecasting.","Answer",{"name":112,"@type":107,"acceptedAnswer":113},"Which models are compared for predicting Ethereum prices?",{"text":114,"@type":110},"The study compares a classical model (ARIMA), a supervised deep learning model (LSTM), and an ensemble machine learning model (XGBoost).",{"name":116,"@type":107,"acceptedAnswer":117},"What variables are used as inputs besides the Ethereum price?",{"text":118,"@type":110},"Inputs include opening, high, low, closing, adjusted closing, traded volume, market capitalization, cumulative return, transactions, blocks, and gas utilized.","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},569624,1792093901,{"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":40,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":140,"read_time":101},1099529839291,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Vol 15, Issue 2,(2025) E-ISSN: 2225-8329  \nBeyond Conventional Methods: Advancing Ethereum Price Prediction through Integrated Technical, On-Chain, and Machine Learning  \nApproaches  \nDalia Elbanna, Ema Izati Zull Kepili 1 , Nik Hadiyan Nik Azman  \nSchool of Management Universiti Sains Malaysia Penang, Malaysia  \nCorresponding Authors Email: [emazull@usm.my](emazull@usm.my)  \nTo Link this Article: [http://dx.doi.org/10.6007/IJARAFMS/v15-i2/24968](http://dx.doi.org/10.6007/IJARAFMS/v15-i2/24968) DOI:10 .6007/IJARAFMS/v15-i2/24968  \nPublished Online: 07 April 2025  \nAbstract  \nEthereum's anonymity and uncontrolled cryptocurrency attraction have attracted investors. Ethereum's price dynamic inspired this study's prediction analyses. Previous study has focused on either technical analysis or on-chain analysis, leaving investors without the synergistic effects of integrating the two. This study addresses missed insights and lack of cross-comparisons by identifying variable relationships and dependencies and comparing a classical model (ARIMA), a supervised deep learning model (LSTM), and an ensemble machine learning model (XGBoost) in Ethereum price prediction. The dependent variable is Ethereum price and the independent variables are opening, high, low, closing, adjusted closing, volume traded, market capitalization, cumulative return, transactions, blocks, and gas utilized. Prices and market capitalization, traded volume, and volume are strongly correlated, and the LSTM model is the most promising due to its greater prediction accuracy and generality. The analysis reveals the bitcoin market's complexity, affecting investing and risk management. Keywords: Cryptocurrency, Ethereum, ARIMA, XGBOOST, LSTM, Technical analysis, On-chain Analysis.  \nIntroduction  \nThe cryptocurrency sector has grown significantly since the creation of Bitcoin in 2009. It evolves from a small technological experiment into a global financial ecosystem. The expansion also includes the increasing development of other cryptocurrencies like Ethereum, LiteCoin, and others. Cryptocurrency is evolving into a multifaceted tool and cases, serving not just as a digital currency but also as an investment vehicle, a method for remittances, anda means of payment. Among these digital assets has evolved from being a form of digital currency to having its own decentralized applications (Dapps) . The market capitalization of cryptocurrencies has increased dramatically due to numerous use cases. The rise of their use and recognition as digital assets has led to different levels of price volatility. Because of the  \n1 Corresponding author’s email: [emazull@usm.my](emazull@usm.my)  \nmarket's inherent volatility and unpredictability, it is becoming more difficult to predict cryptocurrency prices. This increase has sparked a rising interest in precise and dependable methods for predicting cryptocurrency prices (Jagannath et al., 2021) . Not only that, these developments highlight the urgent need for accurate and robust price prediction models, especially as cryptocurrencies become more integrated into mainstream financial portfoliosand investment strategies.  \nStudies pertaining to price forecasting and prediction have progressed from employing fundamental analysis to technical analysis, on-chain analysis, regression analysis, supervised machine learning, and, at present, deep learning techniques. Predicting cryptocurrency prices, particularly Ethereum, is crucial for a wide range of stakeholders—retail and institutional investors, trading platforms, regulators, and financial analysts. For investors, accurate forecasting can reduce risk and enhance returns. For policymakers and institutions, understanding price behavior can inform regulation and financial stability measures. However, the task is complex. Technical analysis, which use historical price and volume data to recognize patterns and trends, have been extensively used in the financial industry for many years. Yet, its u","cbCainGrPxBkimqJ","https://ap.wps.com/l/cbCainGrPxBkimqJ","pdf",706081,"English","# Introduction\n## Background and motivation for cryptocurrency price prediction\n## Prior approaches: technical vs on-chain vs learning models\n# Related work\n## Regression and machine learning methods in cryptocurrency forecasting\n# Method overview\n## Compared models: ARIMA, LSTM, XGBoost","[{\"question\":\"Why does the study integrate technical analysis and on-chain analysis for Ethereum price prediction?\",\"answer\":\"Prior research often focused on either technical indicators or on-chain metrics, which limits the ability to capture synergistic effects. The study combines both to identify relationships and dependencies that support stronger forecasting.\"},{\"question\":\"Which models are compared for predicting Ethereum prices?\",\"answer\":\"The study compares a classical model (ARIMA), a supervised deep learning model (LSTM), and an ensemble machine learning model (XGBoost).\"},{\"question\":\"What variables are used as inputs besides the Ethereum price?\",\"answer\":\"Inputs include opening, high, low, closing, adjusted closing, traded volume, market capitalization, cumulative return, transactions, blocks, and gas utilized.\"}]","Beyond Conventional Methods: Advancing Ethereum Price Prediction through Integrated Technical, On-Chain, and Machine Learning Approaches - Vol 15 - Issue 2 | PDF",1791876736]