{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T19:29:53Z","timestamp":1778354993740,"version":"3.51.4"},"reference-count":51,"publisher":"SAGE Publications","issue":"1-2","license":[{"start":{"date-parts":[[2022,12,20]],"date-time":"2022-12-20T00:00:00Z","timestamp":1671494400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Data Science"],"published-print":{"date-parts":[[2023,12,8]]},"abstract":"<jats:p>Traditional historical data analytics is at risk in a world where volatility, uncertainty, complexity, and ambiguity are the new normal. While Streaming Machine Learning (SML) and Time-series Analytics (TSA) attack some aspects of the problem, we still need a comprehensive solution. SML trains models using fewer data and in a continuous\/adaptive way relaxing the assumption that data points are identically distributed. TSA considers temporal dependence among data points, but it assumes identical distribution. Every Data Scientist fights this battle with ad-hoc solutions. In this paper, we claim that, due to the temporal dependence on the data, the existing solutions do not represent robust solutions to efficiently and automatically keep models relevant even when changes occur, and real-time processing is a must. We propose a novel and solid scientific foundation for Time-Evolving Analytics from this perspective. Such a framework aims to develop the logical, methodological, and algorithmic foundations for fast, scalable, and resilient analytics.<\/jats:p>","DOI":"10.3233\/ds-220057","type":"journal-article","created":{"date-parts":[[2022,12,20]],"date-time":"2022-12-20T12:53:57Z","timestamp":1671540837000},"page":"1-16","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["Towards time-evolving analytics: Online learning for time-dependent evolving data streams"],"prefix":"10.1177","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2768-3580","authenticated-orcid":false,"given":"Giacomo","family":"Ziffer","sequence":"first","affiliation":[{"name":"DEIB, Politecnico di Milano, Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3492-0345","authenticated-orcid":false,"given":"Alessio","family":"Bernardo","sequence":"additional","affiliation":[{"name":"DEIB, Politecnico di Milano, Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5176-5885","authenticated-orcid":false,"given":"Emanuele","family":"Della Valle","sequence":"additional","affiliation":[{"name":"DEIB, Politecnico di Milano, Milano, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9694-8423","authenticated-orcid":false,"given":"Vitor","family":"Cerqueira","sequence":"additional","affiliation":[{"name":"Dalhousie University, Halifax, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8339-7773","authenticated-orcid":false,"given":"Albert","family":"Bifet","sequence":"additional","affiliation":[{"name":"University of Waikato, Hamilton, New Zealand"}]}],"member":"179","published-online":{"date-parts":[[2022,12,20]]},"reference":[{"key":"ref001","unstructured":"O.Anava, E.Hazan, S.Mannor and O.Shamir, Online learning for time series prediction, in:\n                      COLT\n                      , JMLR Workshop and Conference Proceedings, Vol. 30, JMLR.org, 2013, pp. 172\u2013184, available at http:\/\/proceedings.mlr.press\/v30\/Anava13.html."},{"key":"ref002","unstructured":"B.Babcock, M.Datar, R.Motwaniet al., Load shedding techniques for data stream systems, in:\n                      Proceedings of the 2003 Workshop on Management and Processing of Data Streams\n                      , Vol. 577, Citeseer, 2003, available at http:\/\/www-cs-students.stanford.edu\/~datar\/papers\/mpds03.pdf."},{"key":"ref003","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9378396"},{"key":"ref004","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59060-8_64"},{"key":"ref005","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972771.42"},{"key":"ref006","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/10654.001.0001"},{"key":"ref007","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783372"},{"key":"ref008","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40988-2_30"},{"key":"ref009","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-016-9684-8"},{"key":"ref010","unstructured":"G.E.P.Box and G.M.Jenkins,\n                      Time Series Analysis: Forecasting and Control\n                      , John Wiley & Sons, 2015. ISBN 978-1-118-67502-1."},{"key":"ref011","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.11.028"},{"key":"ref012","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3057446"},{"key":"ref013","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-0711-5"},{"key":"ref014","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-70659-3_2"},{"key":"ref015","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-018-1254-7"},{"key":"ref016","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-28645-5_29"},{"key":"ref017","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-012-5320-9"},{"key":"ref018","doi-asserted-by":"publisher","DOI":"10.1145\/2523813"},{"key":"ref019","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmoneco.2008.05.010"},{"key":"ref020","doi-asserted-by":"publisher","DOI":"10.2307\/1912791"},{"key":"ref021","unstructured":"M.Harries and N.S.Wales,\n                      SPLICE-2 Comparative Evaluation: Electricity Pricing\n                      , 1999, available at https:\/\/www.researchgate.net\/publication\/2562830_SPLICE-2_Comparative_Evaluation_Electricity_Pricing."},{"key":"ref022","unstructured":"W.D.Heaven, Our weird behavior during the pandemic is messing with AI models,\n                      MIT Technology Review\n                      (2020), available at https:\/\/www.technologyreview.com\/2020\/05\/11\/1001563\/covid-pandemic-broken-ai-machine-learning-amazon-retail-fraud-humans-in-the-loop\/."},{"key":"ref023","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05318-5"},{"key":"ref024","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0104915"},{"key":"ref025","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2017.02.004"},{"key":"ref026","unstructured":"T.Lee, J.Gottschlich, N.Tatbul, E.Metcalf and S.Zdonik, Greenhouse: A Zero-Positive Machine Learning System for Time-Series Anomaly Detection, 2018, CoRR, arXiv:1801.03168."},{"key":"ref027","doi-asserted-by":"crossref","unstructured":"C.Liu, S.C.H.Hoi, P.Zhao and J.Sun, Online ARIMA algorithms for time series prediction, in:\n                      AAAI\n                      , AAAI Press, 2016, pp. 1867\u20131873, available at https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI16\/paper\/view\/12135.","DOI":"10.1609\/aaai.v30i1.10257"},{"key":"ref028","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2876857"},{"key":"ref029","doi-asserted-by":"publisher","DOI":"10.52591\/lxai201812039"},{"key":"ref030","doi-asserted-by":"publisher","DOI":"10.1201\/9781420059847"},{"key":"ref031","doi-asserted-by":"publisher","DOI":"10.1007\/s00453-015-9974-0"},{"key":"ref032","doi-asserted-by":"publisher","DOI":"10.1016\/C2015-0-04136-3"},{"key":"ref033","unstructured":"K.Panetta, Gartner Top 10 Data and Analytics Trends for 2021, 2021, 2022, available at: https:\/\/www.gartner.com\/smarterwithgartner\/gartner-top-10-data-and-analytics-trends-for-2021."},{"key":"ref034","doi-asserted-by":"publisher","DOI":"10.1109\/5.18626"},{"key":"ref035","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03854-z"},{"key":"ref036","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-61380-8_36"},{"key":"ref037","unstructured":"D.Reinsel, J.Gantz and J.Rydning, The digitization of the world from edge to core,\n                      Framingham: International Data Corporation\n                      16\n                      (2018), available at https:\/\/www.seagate.com\/files\/www-content\/our-story\/trends\/files\/idc-seagate-dataage-whitepaper.pdf."},{"key":"ref038","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref039","doi-asserted-by":"publisher","DOI":"10.2307\/2348250"},{"key":"ref040","doi-asserted-by":"publisher","DOI":"10.1561\/2200000018"},{"key":"ref041","doi-asserted-by":"publisher","DOI":"10.1126\/science.aar6404"},{"key":"ref042","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3122531"},{"key":"ref043","doi-asserted-by":"publisher","DOI":"10.1007\/BFb0091903"},{"key":"ref044","unstructured":"A.Tsymbal, The problem of concept drift: Definitions and related work,\n                      Computer Science Department, Trinity College Dublin\n                      106(2) (2004), 58, available at https:\/\/www.scss.tcd.ie\/publications\/tech-reports\/reports.04\/TCD-CS-2004-15.pdf."},{"key":"ref045","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-3264-1"},{"key":"ref046","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-29135-8_13"},{"key":"ref047","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0477(1998)079<2079:NTASR>2.0.CO;2"},{"key":"ref048","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(20)30260-9"},{"key":"ref049","doi-asserted-by":"publisher","DOI":"10.1016\/j.solener.2018.10.024"},{"key":"ref050","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-014-5441-4"},{"key":"ref051","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-26989-4_4"}],"container-title":["Data 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