[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121707-en":3,"doc-seo-121707-105":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},121707,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Evaluating interpretable machine learning predictions for cryptocurrencies - Published research article overview","This study evaluates interpretable machine learning and deep learning models for cryptocurrency financial data modelling, analysis, and prediction. The work tests prediction accuracy for hourly cryptocurrency returns and examines interpretability features of multiple ML approaches. Six major cryptocurrencies—Bitcoin, Ethereum, Binance Coin, Cardano, Ripple, and Litecoin—are modeled using datasets built from technical, fundamental, and statistical analysis. Algorithms compared include decision trees, random forests, ensembles, SVM, neural networks, N-BEATS variants, ARIMA, and Google AutoML, highlighting strengths and limitations.","Evaluating interpretable machine learning predictions for cryptocurrencies  \nThis is the Published version of the following publication  \nEl Majzoub, Ahmad, Rabhi, Fethi and Hussain, Walayat (2023) Evaluating interpretable machine learning predictions for cryptocurrencies. Intelligent Systems in Accounting, Finance and Management, 30 (3) . pp. 137-149. ISSN 1550-1949  \nThe publisher’s official version can be found at [https://onlinelibrary.wiley.com/doi/10.1002/isaf.1538](https://onlinelibrary.wiley.com/doi/10.1002/isaf.1538)[ ](https://onlinelibrary.wiley.com/doi/10.1002/isaf.1538)Note that access to this version may require subscription.  \nDownloaded from VU Research Repository [https://vuir.vu.edu.au/47984/](https://vuir.vu.edu.au/47984/)  \nReceived: 24 November 2021 Revised: 6 March 2023 Accepted: 29 May 2023  \nDOI: 10.1002/isaf.1538  \nRES EARCH A RTICLE  \nEvaluating interpretable machine learning predictions for cryptocurrencies  \nAhmad El Majzoub 1 | Fethi A. Rabhi 1 | Walayat Hussain 2  \n1School of Computer Science and Engineering, University of New South Wales (UNSW), Kensington, Australia  \n2Peter Faber Business School, Australian Catholic University, North Sydney, Australia  \nCorrespondence  \nWalayat Hussain, Peter Faber Business School, Australian Catholic University, North Sydney, Australia.  \n[Email: walayat.hussain@acu.edu.au](Email: walayat.hussain@acu.edu.au)  \nSummary  \nThis study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N-BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources.  \nKEYWOR DS  \nartificial intelligence, cryptocurrency, deep learning, interpretability, machine learning, technical indicators, time series forecasting  \n1 | INTRODUCTION  \nThe application of artificial intelligence (AI) has been taking various forms within different industries. In finance, numerous firms and banks have been gradually integrating a variety of AI applications into their workflows and processes. These may include automation, credit decisions (Dumitrescu et al., 2022), algorithmic and high-frequency trading, risk management (Hussain, Raza, et al., 2022), fraud detection and prevention (Khan et al., 2022), personalised banking (Cao, 2022) and many others. Despite the inevitable difficulties that companies will be faced with when transitioning into new systems, the potential  \nof AI in transforming the financial sector could not be matched by traditional pipelines. These difficulties may include the large costs associated with R&D and implementation, the business's unrealistic expectations, the shortage of specialised engineers, interpretability and the lack of agility within huge corporations (Dixon et al., 2020) . However, with the exponential increase of computational power and data abundance, the shift into automated intelligent structures powered by machine learning is a crucial step that could determine the survival of many existing corporations (Hussain, ","cbCaivqunlOZBpGT","https://ap.wps.com/l/cbCaivqunlOZBpGT","pdf",4661412,1,14,"English","en",105,"# Summary\n## Research focus and dataset design\n## Algorithms compared and evaluation themes\n## Key findings and practical implications","[{\"question\":\"What is the main goal of the study on cryptocurrency predictions?\",\"answer\":\"The study aims to evaluate the prediction accuracy of cryptocurrency hourly returns and to analyze interpretability features of machine learning models.\"},{\"question\":\"Which cryptocurrencies and data sources are used in the experiments?\",\"answer\":\"Experiments cover Bitcoin, Ethereum, Binance Coin, Cardano, Ripple, and Litecoin, using datasets constructed from technical, fundamental, and statistical analysis.\"},{\"question\":\"Which machine learning and forecasting algorithms are compared?\",\"answer\":\"The paper compares decision trees, random forests and ensemble methods, SVM, neural networks, N-BEATS single and multiple features, ARIMA, and Google AutoML.\"}]","Evaluating interpretable machine learning predictions for cryptocurrencies - Published research article overview | PDF",1785806384,35,{"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},"evaluating-interpretable-machine-learning-predictions-for-cryptocurrencies-published-research-article-overview","",{"@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/evaluating-interpretable-machine-learning-predictions-for-cryptocurrencies-published-research-article-overview/121707/",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 is the main goal of the study on cryptocurrency predictions?","Question",{"text":75,"@type":76},"The study aims to evaluate the prediction accuracy of cryptocurrency hourly returns and to analyze interpretability features of machine learning models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which cryptocurrencies and data sources are used in the experiments?",{"text":80,"@type":76},"Experiments cover Bitcoin, Ethereum, Binance Coin, Cardano, Ripple, and Litecoin, using datasets constructed from technical, fundamental, and statistical analysis.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning and forecasting algorithms are compared?",{"text":84,"@type":76},"The paper compares decision trees, random forests and ensemble methods, SVM, neural networks, N-BEATS single and multiple features, ARIMA, and Google AutoML.","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"]