{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T04:16:29Z","timestamp":1746504989272,"version":"3.28.0"},"reference-count":52,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,10]]},"DOI":"10.1109\/bigdata50022.2020.9378264","type":"proceedings-article","created":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T21:10:21Z","timestamp":1616188221000},"page":"2170-2179","source":"Crossref","is-referenced-by-count":4,"title":["Streaming Time Series Forecasting using Multi-Target Regression with Dynamic Ensemble Selection"],"prefix":"10.1109","author":[{"given":"Dihia","family":"Boulegane","sequence":"first","affiliation":[]},{"given":"Albert","family":"Bifet","sequence":"additional","affiliation":[]},{"given":"Haytham","family":"Elghazel","sequence":"additional","affiliation":[]},{"given":"Giyyarpuram","family":"Madhusudan","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1002\/sam.11461"},{"key":"ref38","first-page":"463","article-title":"Learning comprehensible descriptions of multivariate time series","volume":"454","author":"kadous","year":"1999","journal-title":"ICML"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/1982185.1982402"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1201\/9781315139470"},{"key":"ref31","first-page":"222","article-title":"Constraint based induction of multi-objective regression trees","author":"struyf","year":"2005","journal-title":"International Workshop on Knowledge Discovery in Inductive Databases"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2007.01.025"},{"article-title":"Online multi-target regression trees with stacked leaf models","year":"2019","author":"mastelini","key":"ref37"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/s10844-017-0462-7"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-0865-5_26"},{"article-title":"Top-down induction of clustering trees","year":"2000","author":"blockeel","key":"ref34"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecolmodel.2009.01.037"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-010-0201-y"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9868.00054"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2017.09.010"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-0123-9_3"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46128-1_26"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1157"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71249-9_11"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.39"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1007\/BFb0091924"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-011-5256-5"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","article-title":"Stacked generalization","volume":"5","author":"wolpert","year":"1992","journal-title":"Neural Networks"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.2307\/1391385"},{"key":"ref51","article-title":"Adaptive random forests for data stream regression","author":"gomes","year":"2018","journal-title":"ESANN"},{"key":"ref52","first-page":"9","article-title":"The pairwise multiple comparison of mean ranks package (pmcmr)","volume":"27","author":"pohlert","year":"2014","journal-title":"R Package"},{"key":"ref10","first-page":"1601","article-title":"Moa: Massive online analysis","volume":"11","author":"bifet","year":"2010","journal-title":"Journal of Machine Learning Research"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/2523813"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/502512.502529"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3054925"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/502512.502565"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102408"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.11.053"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2014.10.003"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2013.05.048"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/PL00011679"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICTAI.2010.64"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2014.07.063"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2017.12.008"},{"journal-title":"Metalearning Applications to Data Mining","year":"2008","author":"brazdil","key":"ref6"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2014.05.003"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-018-05774-y"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71246-8_29"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2007.06.001"},{"key":"ref9","first-page":"678","article-title":"A drift-based dynamic ensemble members selection using clustering for time series forecasting","author":"saadallah","year":"2019","journal-title":"Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2015.55"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-017-5642-8"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2008.08.008"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/BigData47090.2019.9005541"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972771.42"},{"key":"ref41","first-page":"249","article-title":"Adaptive learning from evolving data streams","author":"bifet","year":"2009","journal-title":"international symposium on intelligent data analysis"},{"key":"ref44","first-page":"2915","article-title":"Scikit-multiflow: A multi-output streaming framework","volume":"19","author":"montiel","year":"2018","journal-title":"The Journal of Machine Learning Research"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0040"}],"event":{"name":"2020 IEEE International Conference on Big Data (Big Data)","start":{"date-parts":[[2020,12,10]]},"location":"Atlanta, GA, USA","end":{"date-parts":[[2020,12,13]]}},"container-title":["2020 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9377717\/9377728\/09378264.pdf?arnumber=9378264","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T15:53:14Z","timestamp":1656345194000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9378264\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,10]]},"references-count":52,"URL":"https:\/\/doi.org\/10.1109\/bigdata50022.2020.9378264","relation":{},"subject":[],"published":{"date-parts":[[2020,12,10]]}}}