[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126199-en":3,"doc-seo-126199-105":31,"detail-sidebar-cat-0-en-105":93},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126199,549768072016,"River Wang","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Machine Learning Algorithms in Predicting Prices in Volatile Cryptocurrency Markets","This study develops a predictive model for cryptocurrency prices in highly volatile markets, using exploratory data analysis followed by machine learning implementation centered on an LSTM neural network. Model quality is improved through hyperparameter tuning, with stability assessed via analysis of variance (ANOVA). Benchmark results compare LSTM against SVM, XGBoost, and Random Forests, showing clear error reduction. The LSTM achieves strong day-ahead and seven-day accuracy, supported by low RMSE and MAPE values, demonstrating robustness for extended forecasting.","Machine Learning Algorithms in Predicting Prices in Volatile  \nCryptocurrency Markets  \nMiguel Jiménez-Carrión 1 * , Gustavo A. Flores-Fernandez 1  \n1 Faculty of Industrial Engineering, Universidad Nacional de Piura, Castilla-Piura, 20002, Peru.  \nReceived 26 December 2024; Revised 21 February 2025; Accepted 26 February 2025; Published 01 March 2025  \nAbstract  \nThis study aims to develop a predictive model for cryptocurrency prices in highly volatile markets. The methodology includes an exploratory data analysis, followed by designing and implementing machine learning (ML) algorithms, focusing on the Long Short-Term Memory (LSTM) neural network. The model's performance was optimized through hyperparameter tuning, and its stability was validated using an analysis of variance (ANOVA). We conducted a benchmark comparison with other ML approaches. Our LSTM model achieved an R² of 99.41% on the first day of prediction and maintained an accuracy above 97% up to the seventh day, demonstrating its robustness even for extended forecasts. During training, the LSTM model reached an RMSE of $1,187.14 and a MAPE of 2.20%, with the MAPE consistently remaining below 10% during the validation phase. For seven-day forecasts, the model recorded an RMSE of $5,038.46 and a MAPE of 6.83% . In comparison, alternative models such as Support Vector Machines (SVM), Extreme Gradient Boosting (XGBoost), and Random Forests exhibited significantly higher error rates; for instance, XGBoost recorded an RMSE of $17,849.66 and a MAPE of 27.74% . Overall, these findings highlight the superior performance of the LSTM model in addressing the challenges of cryptocurrency price forecasting.  \nKeywords: Neural Networks; Criptocurrencies; Blockchain; Prediction.  \n1. Introduction  \nIn recent years, the efficient use of financial resources has been significantly improved by technological advances. Among these, blockchain technology has emerged as a transformative financial alternative, offering secure and decentralized transactions and creating new opportunities for institutional and individual investors. The cryptocurrency market, driven by blockchain innovations, has grown exponentially, with Bitcoin as the most prominent digital asset, generating the largest monetary volume and influencing global financial dynamics [1, 2] . Despite the rapid expansion of the market, accurately predicting cryptocurrency prices remains a complex and dynamic challenge. Traditional statistical and econometric models, such as autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH), have been widely used for financial time series prediction [3 , 4] . However, these models often struggle to handle cryptocurrency markets’ high volatility and non-linear patterns [5] . As a result, machine learning (ML) approaches, particularly artificial neural networks (ANNs), have gained significant attention for their ability to model complex, nonlinear relationships, as well as pick up subtle market signals [6 , 7] . Recent studies have demonstrated the effectiveness of Long Short-Term Memory (LSTM) networks in financial forecasting, especially for multi-step time series forecasting [8, 9] . However, there is still a gap in the literature regarding the optimization of  \n* [Corresponding author:](Corresponding author: mjimenezc@unp.edu.pe)[ mjimenezc@unp.edu.pe](Corresponding author: mjimenezc@unp.edu.pe)  \n [http://dx.doi.org/10.28991/HIJ-2025-06-01-017](http://dx.doi.org/10.28991/HIJ-2025-06-01-017)  \n➢ This is an open access article under the CC-BY license ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)) .© Authors retain all copyrights.  \nLSTM architectures specifically tailored to the unique characteristics of the cryptocurrency market, including its extreme volatility, external market influences, and investor sentiment.  \nThis study aims to address this gap by designing an LSTM neural netw","cbCaivMsD20dfzk8","https://ap.wps.com/l/cbCaivMsD20dfzk8","pdf",837813,9,1,16,"English","en",105,"# Abstract\n# Introduction\n# Literature Review","[{\"question\":\"What machine learning approach does the study focus on for cryptocurrency price forecasting?\",\"answer\":\"The study centers on a Long Short-Term Memory (LSTM) neural network, optimized through hyperparameter tuning to improve predictive accuracy.\"},{\"question\":\"How is the LSTM model’s performance validated and compared?\",\"answer\":\"Performance is benchmarked against other ML methods such as SVM, XGBoost, and Random Forests. Stability is evaluated using analysis of variance (ANOVA).\"},{\"question\":\"What forecasting results indicate that the LSTM model is robust for volatile markets?\",\"answer\":\"The model reports very high day-one R² and maintains accuracy above 97% through the seventh day, with low RMSE and MAPE metrics, outperforming alternative approaches.\"}]","Machine Learning Algorithms in Predicting Prices in Volatile Cryptocurrency Markets | PDF",1785903751,40,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"machine-learning-algorithms-in-predicting-prices-in-volatile-cryptocurrency-markets","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/machine-learning-algorithms-in-predicting-prices-in-volatile-cryptocurrency-markets/126199/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What machine learning approach does the study focus on for cryptocurrency price forecasting?","Question",{"text":77,"@type":78},"The study centers on a Long Short-Term Memory (LSTM) neural network, optimized through hyperparameter tuning to improve predictive accuracy.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How is the LSTM model’s performance validated and compared?",{"text":82,"@type":78},"Performance is benchmarked against other ML methods such as SVM, XGBoost, and Random Forests. Stability is evaluated using analysis of variance (ANOVA).",{"name":84,"@type":75,"acceptedAnswer":85},"What forecasting results indicate that the LSTM model is robust for volatile markets?",{"text":86,"@type":78},"The model reports very high day-one R² and maintains accuracy above 97% through the seventh day, with low RMSE and MAPE metrics, outperforming alternative approaches.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,117,121,124,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":113,"doc_module":4,"doc_module_name":47,"category_name":114,"show_sort_weight":115,"slug":116},6,"Technology",50,"technology",{"id":118,"doc_module":4,"doc_module_name":47,"category_name":119,"show_sort_weight":30,"slug":120},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":108,"slug":138},19,"General","general"]