[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121560-en":3,"doc-seo-121560-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121560,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Evaluation of the Model for Bitcoin Price Prediction Using Machine Learning Algorithms and Blockchain Technology - Paper","Blockchain technology can be used to analyze and process data through the effective integration of financial resources, while machine learning is a prominent technology for data-driven modeling. This paper combines blockchain and machine learning to predict bitcoin volatility using real-time bitcoin time-series data and a random forest approach. Model performance is evaluated with multiple statistical error metrics, including MAE, RMSE, MAPE, median APE, and sMAPE under different training/test split ratios.","Evaluation of the Model for Bitcoin Price Prediction Using Machine Learning Algorithms and Blockchain Technology  \nMimoza MIJOSKA, Blagoj RISTEVSKI  \nUniversity St. Kliment Ohridski-Bitola, Faculty of Information and Communication Technologies, ul. Partizanska nn, 7000 Bitola, North Macedonia  \n[mijoska.mimoza@uklo.edu.mk](mijoska.mimoza@uklo.edu.mk), [blagoj.ristevski@uklo.edu.mk](blagoj.ristevski@uklo.edu.mk)  \nORCID 0000-0002-4248-2760, ORCID 0000-0002-8356-1203  \nAbstract: Blockchain technology can be used to analyze and process data through the effective integration of financial resources. Likewise, machine learning is one of the most notable technologies in recent years. Both technologies are data-driven, and therefore there is a rapidly growing interest in integrating them for more secure and efficient data sharing and analysis. This paper shows how these two technologies, blockchain technology and machine learning, can be combined to predict bitcoin volatility. To analyze and predict the volatility of bitcoin, real-time series bitcoin data was used, and the random forest algorithm was utilized. To evaluate the model, the following statistical errors were analyzed: mean absolute error, root mean square error, mean absolute percentage error, median absolute percentage error and symmetric mean absolute percentage error in cases using the different split ratios of the training and test sets. The obtained results have shown that the prediction model is well-designed.  \nKeywords: blockchain technology, machine learning, random forests, bitcoin volatility, statistical errors  \n1. Introduction  \nBusinesses are often streamlined and enhanced by the emergence and applicability of new technological advancements. Blockchain is one of those technologies which is bringing a paradigm shift in our various old and traditional business models.  \nBlockchain technology was introduced in 2008 with the publication of Satoshi Nakamoto's paper- \"Bitcoin: a peer-to-peer electronic cash system\" (Nakamoto, 2008) . Blockchain technology was first used in the cryptocurrency Bitcoin. The first Bitcoin transactions took place in January 2009. Apart from their use in the economic domain, bitcoin and blockchain technology solve an important problem in informatics and computer technology that has been an obstacle to building a functional digital monetary system for years. With this technology, the problem of double use is solved, i.e. the risk that the cryptocurrency can be used two or more times is eliminated. Virtual currency developers must prevent users from being able to spend their funds more than once. The  \ninterest of enterprises, industries and governments around the world in blockchain technology is high, as the application of this technology is much larger than the domain of cryptocurrencies.  \nIn 2014, a consortium called R3 was founded to start research and development of blockchain technology. In March 2017, this group counted about 75 companies, and 200 companies in March 2018, to reach 400 companies in March 2022 (Lukić, 2016)(Kramer, 2019) . The formation of such a strong corporation with a lot of research and implementation of blockchain technology, especially in the financial sector, indicates that a new era in the development of banking is coming.  \nThis paper describes the calculation of Bitcoin's realized volatility and discussion of the obtained results. The remainder of the paper is organized as follows. Section 2 highlights the principles of blockchain technology. In the next section, machine learning algorithms are described with particular emphasis on the random forest algorithm used in the research. Section 4 describes the calculation of Bitcoin's realized volatility. The discussion of the obtained results of this research is presented in the fifth section. In the last section are given concluding remarks and directions for further works.  \n2. Blockchain technology  \n\"Block-chain\" is a coined word made up of the words \"bloc","cbCainY18jEKhAIn","https://ap.wps.com/l/cbCainY18jEKhAIn","pdf",335709,1,11,"English","en",105,"# Introduction\n# Blockchain technology\n## Distributed ledger fundamentals\n## Proof-of-Work and consensus\n# Machine learning model and random forest approach\n# Realized volatility calculation\n# Results discussion\n# Conclusions and future work","[{\"question\":\"How does the paper predict bitcoin volatility?\",\"answer\":\"It uses real-time bitcoin time-series data and applies a random forest algorithm to model and predict bitcoin volatility.\"},{\"question\":\"Which statistical error metrics are used to evaluate the model?\",\"answer\":\"The paper evaluates performance using MAE, RMSE, MAPE, median absolute percentage error, and symmetric mean absolute percentage error.\"},{\"question\":\"How is the impact of dataset splitting assessed?\",\"answer\":\"It analyzes results using different split ratios for the training and test sets, then compares the computed error metrics across those configurations.\"}]","Evaluation of the Model for Bitcoin Price Prediction Using Machine Learning Algorithms and Blockchain Technology - Paper | PDF",1785736243,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"evaluation-of-the-model-for-bitcoin-price-prediction-using-machine-learning-algorithms-and-blockchain-technology-paper","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/evaluation-of-the-model-for-bitcoin-price-prediction-using-machine-learning-algorithms-and-blockchain-technology-paper/121560/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the paper predict bitcoin volatility?","Question",{"text":75,"@type":76},"It uses real-time bitcoin time-series data and applies a random forest algorithm to model and predict bitcoin volatility.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which statistical error metrics are used to evaluate the model?",{"text":80,"@type":76},"The paper evaluates performance using MAE, RMSE, MAPE, median absolute percentage error, and symmetric mean absolute percentage error.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the impact of dataset splitting assessed?",{"text":84,"@type":76},"It analyzes results using different split ratios for the training and test sets, then compares the computed error metrics across those configurations.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]