[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118433-en":3,"doc-seo-118433-105":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":4,"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},118433,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Predictive Analysis of Bitcoin Prices Using Advanced Machine Learning Models","The study investigates Bitcoin price forecasting by leveraging advanced machine learning to address the volatility and non-linear dynamics that traditional time-series and econometric methods often fail to model accurately. It positions sentiment analysis and enriched inputs—fundamental information, market sentiment research, and historical price data—as key components for improving predictive precision. Results indicate that soft computing and machine learning deliver the most accurate predictions among current approaches, and highlights potential use of ANN and SVM concepts in financial market forecasting.","Predictive Analysis of Bitcoin Prices Using Advanced  \nMachine Learning Models  \nAshish Kumar Singh1, Dr. Mukesh Kumar2  \n1Research Scholar, Department ofCSE, Rabindra Nath Tagore University, Bhopal, India  \n2Associate Professor, Department ofCSE, Rabindra Nath Tagore University, Bhopal, India  \nAbstract— : The digital currency industry is seeing unprecedented growth this century, drawing traders, investors, and entrepreneurs from all around the globe. It will aid in documenting the routines and behaviours of such a profitable, demanding, and quickly growing industry by offering comparative studies and insights from the pricing data of cryptocurrency exchanges. In 2021, the bitcoin market will hit one of its all-time highs. New exchanges have increased the accessibility of cryptocurrencies, which has increased their appeal. As a result, not only has the interest in and usage of cryptocurrencies grown, but several reputable crypto enterprises have been launched by some of the pioneers. Companies such as Microsoft, Tesla, and Dell are jumping on the bandwagon for virtual currencies, which are becoming increasingly popular. With the proliferation of decentralized digital currencies, it is more important than ever to educate the public about these new assets so that they can make an informed decision about how to invest their money. Results demonstrate that, among the current research methods, soft computing and machine learning provide the most accurate predictions. Lastly, it is said that machine learning techniques such as ANN and SVMs may be employed to forecast changes in the global stock market. Machine learning, back-propagation, forecasting, artificial neural networks, stock markets, feed forward, root-mean-squared errors, and bitcoin forecasting are all related terms.  \nKeywords-Bitcoin Forecasting, Machine Learning, Regression, Artificial Neural Network, Back-propagation, Forecasting, Stock market, Feed forward, RMSE  \nI. INTRODUCTION  \nOne of the first blockchain-based cryptocurrencies was Bitcoin, which was created by Satoshi Nakamoto in 2009. There has been a lot of interest and investment in it because of how decentralized it is and how it may function independently of conventional banks. As a result, investing in Bitcoin is both enticing and hazardous due to its extremely volatile value. In order to make educated judgments, investors, traders, and analysts in the financial sector are always looking for credible ways to predict the value of Bitcoin.  \nDue to its unique properties, Bitcoin value forecasting presents a number of issues. Market mood, technology advancements, regulatory shifts, macroeconomic events, and the rate of cryptocurrency adoption are just a few of the many variables that affect Bitcoin's value, in contrast to more traditional financial assets. Building reliable prediction models is also difficult because Bitcoin has only been around for a short period of time and there is no central regulating body.  \nTraditional time-series analysis, statistical models, and econometric techniques have been largely used in previous studies on Bitcoin value forecasts. Nevertheless, these approaches frequently fail to adequately account for the intricate dynamics of the bitcoin market, leading to subpar accuracy andpredictive capabilities.  \nBy using more sophisticated machine learning algorithmsand better data analysis, this study hopes to overcome the shortcomings of current forecasting systems. In order to improve the precision of forecasts, the suggested method integrates fundamental information, market sentiment research, and price history. We postulate that sentiment analysis, when  \nadded to the forecasting process, would shed light on how market sentiment affects Bitcoin's value and lead to more accurate predictions.  \nWe examine the suggested approach in depth and assess its efficacy using sample data in this study. In this paper, we show how different machine learning methods fared in capturing the funda","cbCaikkr7HR2XUAq","https://ap.wps.com/l/cbCaikkr7HR2XUAq","pdf",291236,1,5,"English","en",105,"# Introduction\n## Challenges in Bitcoin Value Forecasting\n## Proposed Approach and Motivation\n# Related Works\n## Time-Series Analysis and Statistical Models\n## Machine Learning-Based Forecasting","[{\"question\":\"Why is predicting Bitcoin prices considered difficult compared with traditional assets?\",\"answer\":\"Bitcoin value is influenced by many variables such as market mood, technology changes, regulatory shifts, macroeconomic events, and adoption rate. Its short history and lack of a central regulator make reliable model building harder.\"},{\"question\":\"What does the proposed method aim to improve over existing forecasting systems?\",\"answer\":\"It uses more sophisticated machine learning algorithms and better data analysis to overcome shortcomings in accuracy. It integrates fundamental information, market sentiment research, and price history to enhance forecast precision.\"},{\"question\":\"What types of models are commonly used for Bitcoin price prediction in prior research?\",\"answer\":\"Prior work mainly uses three approaches: time-series analysis (e.g., ARIMA, GARCH), statistical/econometric models using macroeconomic variables, and machine learning methods such as neural networks, SVM, and random forests.\"}]","Predictive Analysis of Bitcoin Prices Using Advanced Machine Learning Models | PDF",1785683588,13,{"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},"predictive-analysis-of-bitcoin-prices-using-advanced-machine-learning-models","",{"@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/predictive-analysis-of-bitcoin-prices-using-advanced-machine-learning-models/118433/",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","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is predicting Bitcoin prices considered difficult compared with traditional assets?","Question",{"text":75,"@type":76},"Bitcoin value is influenced by many variables such as market mood, technology changes, regulatory shifts, macroeconomic events, and adoption rate. Its short history and lack of a central regulator make reliable model building harder.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What does the proposed method aim to improve over existing forecasting systems?",{"text":80,"@type":76},"It uses more sophisticated machine learning algorithms and better data analysis to overcome shortcomings in accuracy. It integrates fundamental information, market sentiment research, and price history to enhance forecast precision.",{"name":82,"@type":73,"acceptedAnswer":83},"What types of models are commonly used for Bitcoin price prediction in prior research?",{"text":84,"@type":76},"Prior work mainly uses three approaches: time-series analysis (e.g., ARIMA, GARCH), statistical/econometric models using macroeconomic variables, and machine learning methods such as neural networks, SVM, and random forests.","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},[93,97,101,105,109,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":21,"slug":137},19,"General","general"]