[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123039-en":3,"doc-seo-123039-105":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},123039,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",6,"Technology","CryptoAnalytics - Cryptocoins Price Forecasting with Machine Learning Techniques - Abstract","CryptoAnalytics is a Python software toolkit designed for cryptocoins price forecasting using machine learning time-series models. It targets the challenge of extreme volatility and aims to exploit co-movement patterns and cross-correlation effects across cryptocurrencies. The toolkit enables efficient training and inference on up-to-date trading price data, including dataset retrieval from major aggregators such as CoinMarketCap. It implements RNNs (LSTM, GRU via PyTorch) and GBMs (XGBoost, LightGBM, CatBoost) and follows an open-science approach by releasing its code.","© 2024 by Elsevier B.V. This is the author’s version of the work. The final authenticated version is available online at [https://doi.org/10.1016/j.softx.2024.101663](https://doi.org/10.1016/j.softx.2024.101663) and has been published  \nin SoftwareX, Volume 26, May 2024 .  \nCryptoAnalytics: Cryptocoins Price  \nForecasting with Machine Learning Techniques Pasquale De Rosa Pascal Felber Valerio Schiavoni University of Neuchˆatel, Switzerland, first.last@unine.ch  \nAbstract  \nThis paper introduces CryptoAnalytics, a software toolkit for cryptocoins price forecasting with machine learning (ML) techniques. Cryptocoins are tradable digital assets exchanged for specific trading prices. While history has shown the extreme volatility of such trading prices, the ability to efficiently model and forecast the time series resulting from the exchange price volatility remains an open research challenge. Good results can been achieved with state-of-the-art ML techniques, including Gradient-Boosting Machines (GBMs) and Recurrent Neural Networks (RNNs) . CryptoAnalytics is a software toolkit to easily train these models and make inference on up-to-date cryptocoin trading price data, with facilities to fetch datasets from oneof the main leading aggregator websites, i. e., CoinMarketCap, train models and infer the future trends. This software is implemented in Python. It relies on PyTorch for the implementation of RNNs (LSTMand GRU), while for GBMs, it leverages on XgBoost, LightGBM and CatBoost. We follow an open science approach and release our code in the project repository.  \n1 Motivation and Significance  \n1.1 Introduction to CryptoAnalytics  \nCryptocoins are digitally-encrypted assets, used mostly in peer-to-peer networks. Depending on the underlying blockchain, cryptocoins are rewarded to nodes in the network. History has shown the extreme volatility of cryptocoins trading prices. In the first place, one could consider these price trends  \nunpredictable and the resulting time series as a random walk. However, recent studies [1, 2] revealed the presence of co-movement among different coins and cross-correlation phenomena in cryptocoins market prices trends. The main purpose of CryptoAnalytics is to provide third-party clients with a fast and reliable tool to leverage these co-movement patterns in order to forecast cryptocoins prices. CryptoAnalytics implements a wide range of state-of-the-art ML techniques for time series forecasting (LSTM [3], GRU [4], XgBoost [5], LightGBM [6] or CatBoost [7]) in order to predict the market price of the desired cryptocurrency basing on other closely-related coins.  \n1.2 Objective of CryptoAnalytics  \nThe goal of CryptoAnalytics is to provide a wide range of potential clients (like investors, institutions and/or goverments) with a reliable and easy-to-implement service to forecast cryptocoins prices. The crypto market is characterized by an extreme volatility, with sudden and continous changes in trading prices, as we will further discuss in subsection 1 .5. With CryptoAnalytics it is possible to handle these complexities using an efficient and scalable tool. Moreover, we extend our analysis to the deployment of CryptoAnalytics into a production environment (section 4), making it possible to design a cryptocurrency prediction service designed to be adopted by either technical and non-technical users, with a very limited (or no) knowledge of ML algorithms.  \n1.3 Scientific Contribution  \nCryptoAnalytics has been used in the context of two scientific papers, presented respectively at the IFIP/DAIS 2022 [8] and at the ACM/DEBS 2023 [9] conferences. In [8], CryptoAnalytics was leveraged to investigate daily, weekly and monthly correlation patterns exhibited by the two main cryptocoins, Bitcoin and Ethereum, against a remaining set of 66 altcoins. Moreover, in [9], CryptoAnalytics was used to study the trend correlations between and across a large set of 62 cryptocoins and subsequently to forecast Ethereum and ","cbCaikeIptTa99U0","https://ap.wps.com/l/cbCaikeIptTa99U0","pdf",379997,1,15,"English","en",105,"# Motivation and Significance\n## Introduction to CryptoAnalytics\n## Objective of CryptoAnalytics\n## Scientific Contribution\n## Theoretical Foundations\n### Gradient-Boosting Machines\n### Recurrent Neural Networks","[{\"question\":\"What problem does CryptoAnalytics address in cryptocurrency markets?\",\"answer\":\"It addresses the difficulty of modeling and forecasting time series caused by the extreme volatility of cryptocoins trading prices.\"},{\"question\":\"Which machine learning model families does CryptoAnalytics support?\",\"answer\":\"It supports Gradient-Boosting Machines (GBMs) and Recurrent Neural Networks (RNNs) adapted for cryptocoins price forecasting.\"},{\"question\":\"How does CryptoAnalytics help users obtain data and produce forecasts?\",\"answer\":\"It provides tools to fetch datasets from leading aggregators like CoinMarketCap, train models, and run inference on up-to-date trading price data to predict future trends.\"}]","CryptoAnalytics - Cryptocoins Price Forecasting with Machine Learning Techniques - Abstract | PDF",1785814334,38,{"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},"cryptoanalytics-cryptocoins-price-forecasting-with-machine-learning-techniques-abstract","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/cryptoanalytics-cryptocoins-price-forecasting-with-machine-learning-techniques-abstract/123039/",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","2026-08-04",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},"What problem does CryptoAnalytics address in cryptocurrency markets?","Question",{"text":75,"@type":76},"It addresses the difficulty of modeling and forecasting time series caused by the extreme volatility of cryptocoins trading prices.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning model families does CryptoAnalytics support?",{"text":80,"@type":76},"It supports Gradient-Boosting Machines (GBMs) and Recurrent Neural Networks (RNNs) adapted for cryptocoins price forecasting.",{"name":82,"@type":73,"acceptedAnswer":83},"How does CryptoAnalytics help users obtain data and produce forecasts?",{"text":84,"@type":76},"It provides tools to fetch datasets from leading aggregators like CoinMarketCap, train models, and run inference on up-to-date trading price data to predict future trends.","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},[93,97,101,105,110,113,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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"]