[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123796-en":3,"doc-seo-123796-105":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},123796,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Time Series Forecasting Utilizing Automated Machine Learning (AutoML) - A Comparative Analysis Study on Diverse Datasets","Automated Machine Learning (AutoML) tools are transforming machine learning by lowering the need for deep technical expertise and enabling users to build high-performing models more accessibly. This study applies AutoML to time series analysis, where historical data are used to forecast future trends. Three AutoML tools—AutoGluon, Auto-Sklearn, and PyCaret—are evaluated on diverse datasets including Bitcoin and COVID-19 data. Findings show that results strongly depend on the dataset characteristics and the ability to handle time-series complexity, emphasizing dataset-specific considerations for practitioners and future research.","UC Riverside  \nUC Riverside Previously Published Works  \nTitle  \nTime Series Forecasting Utilizing Automated Machine Learning (AutoML): A Comparative Analysis Study on Diverse Datasets  \nPermalink  \n[https://escholarship.org/uc/item/7zk102mt](https://escholarship.org/uc/item/7zk102mt)  \nJournal  \nINFORMATION, 15(1)  \nAuthors  \nWestergaard, George  \nErden, Utku  \nMateo, Omar Abdallah et al.  \nPublication Date  \n2024  \nDOI  \n10.3390/info15010039  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons AttributionNonCommercial-NoDerivatives License, available at [https://creativecommons.org/licenses/by-nc-nd/4.0/](https://creativecommons.org/licenses/by-nc-nd/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \n information   \nArticle  \nTime Series Forecasting Utilizing Automated Machine Learning (AutoML): A Comparative Analysis Study on Diverse Datasets  \nGeorge Westergaard 1, Utku Erden 1, Omar Abdallah Mateo 1, Sullaiman Musah Lampo 1, Tahir Cetin Akinci 2,3 and Oguzhan Topsakal 1, *  \nCitation: Westergaard, G.; Erden, U.; Mateo, O.A.; Lampo, S.M.; Akinci, T.C.; Topsakal, O. Time Series Forecasting Utilizing Automated Machine Learning (AutoML): A Comparative Analysis Study on Diverse Datasets. Information 2024, 15, 39. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)info15010039  \nAcademic Editor: Binbin Yong  \nReceived: 20 November 2023  \nRevised: 7 January 2024  \nAccepted: 10 January 2024  \nPublished: 11 January 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Computer Science, Florida Polytechnic University, Lakeland, FL 33805, USA  \n2 Electrical Engineering Department, Istanbul Technical University, Istanbul 34467, Turkey  \n3 Winston Chung Global Energy Center (WCGEC), University of California at Riverside (UCR), Riverside, CA 92521, USA  \n* [Correspondence: otopsakal@floridapily.edu](Correspondence: otopsakal@floridapily.edu)  \nAbstract: Automated Machine Learning (AutoML) tools are revolutionizing the field of machine learning by significantly reducing the need for deep computer science expertise. Designed to make ML more accessible, they enable users to build high-performing models without extensive technical knowledge. This study delves into these tools in the context of time series analysis, which is essential for forecasting future trends from historical data. We evaluate three prominent AutoML tools—AutoGluon, Auto-Sklearn, and PyCaret—across various metrics, employing diverse datasets that include Bitcoin and COVID-19 data. The results reveal that the performance of each tool is highly dependent on the specific dataset and its ability to manage the complexities of time series data. This thorough investigation not only demonstrates the strengths and limitations of each AutoML tool but also highlights the criticality of dataset-specific considerations in time series analysis. Offering valuable insights for both practitioners and researchers, this study emphasizes the ongoing need for research and development in this specialized area. It aims to serve as a reference for organizations dealing with time series datasets and a guiding framework for future academic research in enhancing the application of AutoML tools for time series forecasting and analysis.  \nKeywords: forecasting; time series; AutoML; machine learning; cryptocurrency; COVID-19; Bitcoin; weather; AutoGluon; Auto-Sklearn; PyCaret  \n1. Introduction  \nTime series data are characterized as datasets aligned in a chronological sequence over a specified temporal interval [1] . Such data ar","cbCaisFeX6E2SQAO","https://ap.wps.com/l/cbCaisFeX6E2SQAO","pdf",3429292,1,21,"English","en",105,"# Introduction\n## Time series characteristics\n## Motivation for machine learning\n## Time series tasks (forecasting, anomaly detection, imputation, classification)","[{\"question\":\"Why is time series forecasting important in this study?\",\"answer\":\"Time series forecasting uses historical chronological data to predict future values and trends, which is central to the study’s evaluation of AutoML tools in time-series contexts.\"},{\"question\":\"Which AutoML tools are compared in the research?\",\"answer\":\"The study evaluates AutoGluon, Auto-Sklearn, and PyCaret using multiple metrics across several diverse datasets.\"},{\"question\":\"What determines the performance of each AutoML tool?\",\"answer\":\"The results indicate that performance is highly dependent on the specific dataset and how well it manages time-series complexities.\"}]","Time Series Forecasting Utilizing Automated Machine Learning (AutoML) - A Comparative Analysis Study on Diverse Datasets | PDF",1785818610,53,{"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},"time-series-forecasting-utilizing-automated-machine-learning-automl-a-comparative-analysis-study-on-diverse-datasets","",{"@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/time-series-forecasting-utilizing-automated-machine-learning-automl-a-comparative-analysis-study-on-diverse-datasets/123796/",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},"Why is time series forecasting important in this study?","Question",{"text":75,"@type":76},"Time series forecasting uses historical chronological data to predict future values and trends, which is central to the study’s evaluation of AutoML tools in time-series contexts.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which AutoML tools are compared in the research?",{"text":80,"@type":76},"The study evaluates AutoGluon, Auto-Sklearn, and PyCaret using multiple metrics across several diverse datasets.",{"name":82,"@type":73,"acceptedAnswer":83},"What determines the performance of each AutoML tool?",{"text":84,"@type":76},"The results indicate that performance is highly dependent on the specific dataset and how well it manages time-series complexities.","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"]