EnsPKDE&IncLKDE: a hybrid time series prediction algorithm integrating dynamic ensemble pruning, incremental learning, and kernel density estimation

Ensemble pruning can effectively overcome several shortcomings of the classical ensemble learning paradigm, such as the relatively high time and space complexity. However, each predictor has its own unique ability. One predictor may not perform well on some samples, but it will perform very well on...

Full description

Saved in:

Bibliographic Details
Published in:	Applied intelligence (Dordrecht, Netherlands) Vol. 51; no. 2; pp. 617 - 645
Main Authors:	Zhu, Gangliang, Dai, Qun
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.02.2021 Springer Nature B.V
Subjects:	Algorithms Artificial Intelligence Computer Science Density distribution Kernels Machine learning Machines Manufacturing Mechanical Engineering Performance prediction Processes Time series Traveling salesman problem One-step-ahead prediction Kernel density estimation (KDE) Incremental learning (IL) Time series prediction (TSP) Dynamic ensemble pruning (DEP)
ISSN:	0924-669X, 1573-7497
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Ensemble pruning can effectively overcome several shortcomings of the classical ensemble learning paradigm, such as the relatively high time and space complexity. However, each predictor has its own unique ability. One predictor may not perform well on some samples, but it will perform very well on other samples. Blindly underestimating the power of specific predictors is unreasonable. Choosing the best predictor set for each query sample is exactly what dynamic ensemble pruning techniques address. This paper proposes a hybrid Time Series Prediction (TSP) algorithm to implement one-step-ahead prediction task, integrating Dynamic Ensemble Pruning (DEP), Incremental Learning (IL), and Kernel Density Estimation (KDE), abbreviated as the EnsP KDE &IncL KDE algorithm. It dynamically selects proper predictor sets based on the kernel density distribution of all base learners’ prediction values. Due to the characteristic of TSP problems that samples arrive in chronological order, the idea of IL is naturally introduced into EnsP KDE &IncL KDE , while DEP is a common technology to address the concept drift issue inherent in IL. The algorithm is divided into three subprocesses: 1) Overproduction, which generates the original ensemble learning system; 2) Dynamic Ensemble Pruning (DEP), achieved by one subalgorithm called EnsP KDE ; 3) Incremental Learning (IL), realized by one subalgorithm termed IncL KDE . Benefited from the advantages of integrating Dynamic Ensemble Pruning scheme, Incremental Learning paradigm and Kernel Density Estimation, in the experimental results, EnsP KDE &IncL KDE demonstrates superior prediction performance to several other state-of-the-art algorithms in fulfilling time series forecasting tasks.
AbstractList	Ensemble pruning can effectively overcome several shortcomings of the classical ensemble learning paradigm, such as the relatively high time and space complexity. However, each predictor has its own unique ability. One predictor may not perform well on some samples, but it will perform very well on other samples. Blindly underestimating the power of specific predictors is unreasonable. Choosing the best predictor set for each query sample is exactly what dynamic ensemble pruning techniques address. This paper proposes a hybrid Time Series Prediction (TSP) algorithm to implement one-step-ahead prediction task, integrating Dynamic Ensemble Pruning (DEP), Incremental Learning (IL), and Kernel Density Estimation (KDE), abbreviated as the EnsPKDE&IncLKDE algorithm. It dynamically selects proper predictor sets based on the kernel density distribution of all base learners’ prediction values. Due to the characteristic of TSP problems that samples arrive in chronological order, the idea of IL is naturally introduced into EnsPKDE&IncLKDE, while DEP is a common technology to address the concept drift issue inherent in IL. The algorithm is divided into three subprocesses: 1) Overproduction, which generates the original ensemble learning system; 2) Dynamic Ensemble Pruning (DEP), achieved by one subalgorithm called EnsPKDE; 3) Incremental Learning (IL), realized by one subalgorithm termed IncLKDE. Benefited from the advantages of integrating Dynamic Ensemble Pruning scheme, Incremental Learning paradigm and Kernel Density Estimation, in the experimental results, EnsPKDE&IncLKDE demonstrates superior prediction performance to several other state-of-the-art algorithms in fulfilling time series forecasting tasks. Ensemble pruning can effectively overcome several shortcomings of the classical ensemble learning paradigm, such as the relatively high time and space complexity. However, each predictor has its own unique ability. One predictor may not perform well on some samples, but it will perform very well on other samples. Blindly underestimating the power of specific predictors is unreasonable. Choosing the best predictor set for each query sample is exactly what dynamic ensemble pruning techniques address. This paper proposes a hybrid Time Series Prediction (TSP) algorithm to implement one-step-ahead prediction task, integrating Dynamic Ensemble Pruning (DEP), Incremental Learning (IL), and Kernel Density Estimation (KDE), abbreviated as the EnsP KDE &IncL KDE algorithm. It dynamically selects proper predictor sets based on the kernel density distribution of all base learners’ prediction values. Due to the characteristic of TSP problems that samples arrive in chronological order, the idea of IL is naturally introduced into EnsP KDE &IncL KDE , while DEP is a common technology to address the concept drift issue inherent in IL. The algorithm is divided into three subprocesses: 1) Overproduction, which generates the original ensemble learning system; 2) Dynamic Ensemble Pruning (DEP), achieved by one subalgorithm called EnsP KDE ; 3) Incremental Learning (IL), realized by one subalgorithm termed IncL KDE . Benefited from the advantages of integrating Dynamic Ensemble Pruning scheme, Incremental Learning paradigm and Kernel Density Estimation, in the experimental results, EnsP KDE &IncL KDE demonstrates superior prediction performance to several other state-of-the-art algorithms in fulfilling time series forecasting tasks.
Author	Zhu, Gangliang Dai, Qun
Author_xml	– sequence: 1 givenname: Gangliang surname: Zhu fullname: Zhu, Gangliang organization: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics – sequence: 2 givenname: Qun surname: Dai fullname: Dai, Qun email: daiqun@nuaa.edu.cn organization: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics
BookMark	eNp9kMFOGzEQhi1EJULgBXqyVIlTtx17zdrLDUFoEZHKAaTeLK89CYZdb2o7h7xHH7hOtlKlHjiNxvN__mf-U3IcxoCEfGTwhQHIr4mBUG0FHCpgCngljsiMXcq6kqKVx2QGLRdV07Q_T8hpSq8AUNfAZuT3IqTHh9vFxX2wy1KvqKEvuy56R7MfkCaMHhPdRHTeZj8Gavr1GH1-GagPGdfRZB_W1O2CGbylGBIOXY-F2IYy-FxUNuKAIZue9mji9GqCo28YA_bUFcbnHcVUHM3e44x8WJk-4fnfOifPd4unm-_V8se3-5vrZWW5aHO1Yg6l5bZT1lohhLuUFiTrVCe4U6WXVqFSrGaNsZ3jYIxowUknmsYqhvWcfJr-3cTx17b469dxG0Ox1FzIRgrGeFtUfFLZOKYUcaU3sSwad5qB3qevp_R1SV8f0teiQOo_yPp8OC5H4_v30XpCU_EJa4z_tnqH-gN0BJ5z
CitedBy_id	crossref_primary_10_1016_j_jksuci_2024_102180 crossref_primary_10_1016_j_ins_2023_119103 crossref_primary_10_1007_s10489_021_02385_4 crossref_primary_10_1007_s11042_024_18329_2 crossref_primary_10_1007_s10489_022_03314_9 crossref_primary_10_1007_s10489_023_04572_x crossref_primary_10_1016_j_eswa_2023_120148 crossref_primary_10_1109_ACCESS_2021_3101741 crossref_primary_10_1007_s44443_025_00030_5 crossref_primary_10_1109_ACCESS_2022_3169785 crossref_primary_10_1007_s10489_022_03855_z crossref_primary_10_1002_sam_70046
Cites_doi	10.1016/j.energy.2016.07.092 10.1007/978-3-030-18058-4_13 10.1007/s00521-018-3434-0 10.1609/aaai.v32i1.11836 10.1016/j.eswa.2017.04.013 10.1109/IJCNN.2015.7280528 10.1016/j.eswa.2014.08.018 10.1016/S0166-4115(97)80111-2 10.1016/j.patcog.2011.03.020 10.1016/j.ijforecast.2011.04.001 10.1007/s00521-017-3096-3 10.1093/mnras/stv632 10.1145/1557019.1557060 10.1016/j.patcog.2014.12.003 10.1016/j.neucom.2014.05.068 10.1109/TNNLS.2015.2404823 10.1016/j.neucom.2005.12.126 10.1109/ICNN.1993.298533 10.1016/j.ins.2015.07.020 10.1016/S0004-3702(02)00190-X 10.1109/TNN.2006.880583 10.1016/j.knosys.2016.05.031 10.1016/j.asoc.2017.12.032 10.1162/neco.1997.9.8.1735 10.1609/aaai.v31i1.10806 10.1109/TNN.2008.2008326 10.1007/s00521-012-0873-x 10.1016/j.knosys.2018.05.021 10.1016/j.eswa.2016.06.035 10.1207/s15516709cog1402_1 10.1109/5326.983933 10.1109/TSMCB.2011.2168604 10.1007/s00500-012-0824-6 10.1109/78.782193 10.1109/CIEL.2014.7015739 10.1109/78.650093 10.1109/TNN.2011.2169087 10.1214/aoms/1177704472 10.1007/978-1-4757-2281-9_15 10.1016/j.neucom.2011.12.064 10.1007/s11063-018-9957-7
ContentType	Journal Article
Copyright	Springer Science+Business Media, LLC, part of Springer Nature 2020 Springer Science+Business Media, LLC, part of Springer Nature 2020.
Copyright_xml	– notice: Springer Science+Business Media, LLC, part of Springer Nature 2020 – notice: Springer Science+Business Media, LLC, part of Springer Nature 2020.
DBID	AAYXX CITATION 3V. 7SC 7WY 7WZ 7XB 87Z 8AL 8FD 8FE 8FG 8FK 8FL ABJCF ABUWG AFKRA ARAPS AZQEC BENPR BEZIV BGLVJ CCPQU DWQXO FRNLG F~G GNUQQ HCIFZ JQ2 K60 K6~ K7- L.- L6V L7M L~C L~D M0C M0N M7S P5Z P62 PHGZM PHGZT PKEHL PQBIZ PQBZA PQEST PQGLB PQQKQ PQUKI PRINS PSYQQ PTHSS Q9U
DOI	10.1007/s10489-020-01802-4
DatabaseName	CrossRef ProQuest Central (Corporate) Computer and Information Systems Abstracts ABI/INFORM Collection ABI/INFORM Global (PDF only) ProQuest Central (purchase pre-March 2016) ABI/INFORM Collection Computing Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) (purchase pre-March 2016) ABI/INFORM Collection (Alumni) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Business Premium Collection Technology Collection ProQuest One Community College ProQuest Central Business Premium Collection (Alumni) ABI/INFORM Global (Corporate) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection ProQuest Business Collection (Alumni Edition) ProQuest Business Collection Computer Science Database ABI/INFORM Professional Advanced ProQuest Engineering Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional ABI/INFORM Global Computing Database Engineering Database Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Premium ProQuest One Academic ProQuest One Academic Middle East (New) ProQuest One Business ProQuest One Business (Alumni) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest One Psychology Engineering Collection ProQuest Central Basic
DatabaseTitle	CrossRef ProQuest Business Collection (Alumni Edition) ProQuest One Psychology Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China ABI/INFORM Complete ProQuest One Applied & Life Sciences ProQuest Central (New) Engineering Collection Advanced Technologies & Aerospace Collection Business Premium Collection ABI/INFORM Global Engineering Database ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest Business Collection ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ABI/INFORM Global (Corporate) ProQuest One Business Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Central ABI/INFORM Professional Advanced ProQuest Engineering Collection ProQuest Central Korea Advanced Technologies Database with Aerospace ABI/INFORM Complete (Alumni Edition) ProQuest Computing ABI/INFORM Global (Alumni Edition) ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database Materials Science & Engineering Collection ProQuest One Business (Alumni) ProQuest Central (Alumni) Business Premium Collection (Alumni)
DatabaseTitleList	ProQuest Business Collection (Alumni Edition)
Database_xml	– sequence: 1 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Computer Science
EISSN	1573-7497
EndPage	645
ExternalDocumentID	10_1007_s10489_020_01802_4
GrantInformation_xml	– fundername: National Key R&D Program of China grantid: 2018YFC2001600, 2018YFC2001602 – fundername: National Natural Science Foundation of China grantid: 61473150
GroupedDBID	-4Z -59 -5G -BR -EM -Y2 -~C -~X .86 .DC .VR 06D 0R~ 0VY 1N0 1SB 2.D 203 23M 28- 2J2 2JN 2JY 2KG 2LR 2P1 2VQ 2~H 30V 3V. 4.4 406 408 409 40D 40E 5GY 5QI 5VS 67Z 6NX 77K 7WY 8FE 8FG 8FL 8TC 8UJ 95- 95. 95~ 96X AAAVM AABHQ AACDK AAHNG AAIAL AAJBT AAJKR AANZL AAOBN AARHV AARTL AASML AATNV AATVU AAUYE AAWCG AAYIU AAYQN AAYTO AAYZH ABAKF ABBBX ABBXA ABDZT ABECU ABFTV ABHLI ABHQN ABIVO ABJCF ABJNI ABJOX ABKCH ABKTR ABMNI ABMQK ABNWP ABQBU ABQSL ABSXP ABTAH ABTEG ABTHY ABTKH ABTMW ABULA ABUWG ABWNU ABXPI ACAOD ACBXY ACDTI ACGFS ACHSB ACHXU ACIWK ACKNC ACMDZ ACMLO ACOKC ACOMO ACPIV ACSNA ACZOJ ADHHG ADHIR ADIMF ADINQ ADKNI ADKPE ADRFC ADTPH ADURQ ADYFF ADZKW AEBTG AEFIE AEFQL AEGAL AEGNC AEJHL AEJRE AEKMD AEMSY AENEX AEOHA AEPYU AESKC AETLH AEVLU AEXYK AFBBN AFEXP AFGCZ AFKRA AFLOW AFQWF AFWTZ AFZKB AGAYW AGDGC AGGDS AGJBK AGMZJ AGQEE AGQMX AGRTI AGWIL AGWZB AGYKE AHAVH AHBYD AHKAY AHSBF AHYZX AIAKS AIGIU AIIXL AILAN AITGF AJBLW AJRNO AJZVZ ALMA_UNASSIGNED_HOLDINGS ALWAN AMKLP AMXSW AMYLF AMYQR AOCGG ARAPS ARMRJ ASPBG AVWKF AXYYD AYJHY AZFZN AZQEC B-. BA0 BBWZM BDATZ BENPR BEZIV BGLVJ BGNMA BPHCQ BSONS CAG CCPQU COF CS3 CSCUP DDRTE DL5 DNIVK DPUIP DWQXO EBLON EBS EIOEI EJD ESBYG FEDTE FERAY FFXSO FIGPU FINBP FNLPD FRNLG FRRFC FSGXE FWDCC GGCAI GGRSB GJIRD GNUQQ GNWQR GQ6 GQ7 GQ8 GROUPED_ABI_INFORM_COMPLETE GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HQYDN HRMNR HVGLF HZ~ I09 IHE IJ- IKXTQ ITM IWAJR IXC IZIGR IZQ I~X I~Z J-C J0Z JBSCW JCJTX JZLTJ K60 K6V K6~ K7- KDC KOV KOW L6V LAK LLZTM M0C M0N M4Y M7S MA- N2Q N9A NB0 NDZJH NPVJJ NQJWS NU0 O9- O93 O9G O9I O9J OAM OVD P19 P2P P62 P9O PF0 PQBIZ PQBZA PQQKQ PROAC PSYQQ PT4 PT5 PTHSS Q2X QOK QOS R4E R89 R9I RHV RNI RNS ROL RPX RSV RZC RZE RZK S16 S1Z S26 S27 S28 S3B SAP SCJ SCLPG SCO SDH SDM SHX SISQX SJYHP SNE SNPRN SNX SOHCF SOJ SPISZ SRMVM SSLCW STPWE SZN T13 T16 TEORI TSG TSK TSV TUC U2A UG4 UOJIU UTJUX UZXMN VC2 VFIZW W23 W48 WK8 YLTOR Z45 Z7R Z7X Z7Z Z81 Z83 Z88 Z8M Z8N Z8R Z8T Z8U Z8W Z92 ZMTXR ZY4 ~A9 ~EX 77I AAPKM AAYXX ABBRH ABDBE ABFSG ABRTQ ACSTC ADHKG ADKFA AEZWR AFDZB AFFHD AFHIU AFOHR AGQPQ AHPBZ AHWEU AIXLP ATHPR AYFIA CITATION PHGZM PHGZT PQGLB 7SC 7XB 8AL 8FD 8FK JQ2 L.- L7M L~C L~D PKEHL PQEST PQUKI PRINS Q9U
ID	FETCH-LOGICAL-c249t-f1de7c2cb8ccc444d57c071b8b42d844d7c8e881316acbd20aa490d7d466c81e3
IEDL.DBID	M7S
ISICitedReferencesCount	12
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000561725100002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0924-669X
IngestDate	Wed Nov 05 14:51:19 EST 2025 Sat Nov 29 05:33:21 EST 2025 Tue Nov 18 21:50:00 EST 2025 Fri Feb 21 02:26:56 EST 2025
IsPeerReviewed	true
IsScholarly	true
Issue	2
Keywords	One-step-ahead prediction Kernel density estimation (KDE) Incremental learning (IL) Time series prediction (TSP) Dynamic ensemble pruning (DEP)
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c249t-f1de7c2cb8ccc444d57c071b8b42d844d7c8e881316acbd20aa490d7d466c81e3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
PQID	2476741129
PQPubID	326365
PageCount	29
ParticipantIDs	proquest_journals_2476741129 crossref_primary_10_1007_s10489_020_01802_4 crossref_citationtrail_10_1007_s10489_020_01802_4 springer_journals_10_1007_s10489_020_01802_4
PublicationCentury	2000
PublicationDate	20210200 2021-02-00 20210201
PublicationDateYYYYMMDD	2021-02-01
PublicationDate_xml	– month: 2 year: 2021 text: 20210200
PublicationDecade	2020
PublicationPlace	New York
PublicationPlace_xml	– name: New York – name: Boston
PublicationSubtitle	The International Journal of Research on Intelligent Systems for Real Life Complex Problems
PublicationTitle	Applied intelligence (Dordrecht, Netherlands)
PublicationTitleAbbrev	Appl Intell
PublicationYear	2021
Publisher	Springer US Springer Nature B.V
Publisher_xml	– name: Springer US – name: Springer Nature B.V
References	GaxiolaFMelinPValdezFCastilloOGeneralized type-2 fuzzy weight adjustment for backpropagation neural networks in time series predictionInf Sci201532515917433922961328.6816410.1016/j.ins.2015.07.020 HintonGESrivastavaNKrizhevskyASutskeverISalakhutdinovRRImproving neural networks by preventing co-adaptation of feature detectorsComput Sci20124212223 ZhouTGaoSWangJChuCTodoYTangZFinancial time series prediction using a dendritic neuron modelKnowl-Based Syst201610521422410.1016/j.knosys.2016.05.031 LinLFangWXieXZhongSRandom forests-based extreme learning machine ensemble for multi-regime time series predictionExpert Syst Appl20178316417610.1016/j.eswa.2017.04.013 M. C. A. Neto, G. D. C. Cavalcanti, and I. R. Tsang, "Financial time series prediction using exogenous series and combined neural networks," in International Joint Conference on Neural Networks, pp. 2578–2585, 2009 LiangN-YHuangG-BSaratchandranPSundararajanNA fast and accurate online sequential learning algorithm for feedforward networksIEEE Trans Neural Netw2006171411142310.1109/TNN.2006.880583 WoloszynskiTKurzynskiMA probabilistic model of classifier competence for dynamic ensemble selectionPattern Recogn201144265626681218.6815510.1016/j.patcog.2011.03.020 HuangGBZhuQYSiewCKExtreme learning machine: a new learning scheme of feedforward neural networksIEEE Int Joint Confer Neural Networks20052985990 LimJSLeeSPangHSLow complexity adaptive forgetting factor for online sequential extreme learning machine (OS-ELM) for application to nonstationary system estimationsNeural Comput Applic20132256957610.1007/s00521-012-0873-x Carlo E. Bonferroni, "Il calcolo delle assicurazioni su gruppi di teste," In Studi in Onore del Professore Salvatore Ortu Carboni, Rome: Italy, pp. 13–60, 1935 E. Ley and M. F. Steel (1993) Bayesian econometrics: Conjugate analysis and rejection sampling, in Economic and Financial Modeling with Mathematica®, ed: Springer, pp. 344–367 H. Yao, F. Wu, J. Ke, X. Tang, Y. Jia, S. Lu, et al.(2018) Deep multi-view spatial-temporal network for taxi demand prediction," in Thirty-Second AAAI Conference on Artificial Intelligence CroneSFHibonMNikolopoulosKAdvances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series predictionInt J Forecast20112763566010.1016/j.ijforecast.2011.04.001 W. Hong, F. Wei, F. Sun, and X. Qian (2015) An adaptive ensemble model of extreme learning machine for time series prediction," in International Computer Conference on Wavelet Active Media Technology & Information Processing, pp. 80–85 ZhouZHWuJTangWEnsembling neural networks: many could be better than allArtif Intell200213723926319064770995.6807710.1016/S0004-3702(02)00190-X HuangGBZhuQYSiewCKExtreme learning machine: theory and applicationsNeurocomputing20067048950110.1016/j.neucom.2005.12.126 BodyanskiyYVTyshchenkoOKA hybrid Cascade neural network with ensembles of extended neo-fuzzy neurons and its deep learningInf Technol Syst Res Comput Phys202094516417410.1007/978-3-030-18058-4_13 M. I. Jordan, "Serial order: A parallel distributed processing approach," in Advances in Psychology. vol. 121, ed: Elsevier, 1997, pp. 471–495 BezerraCGCostaBSJGuedesLAAngelovPPAn evolving approach to unsupervised and real-time fault detection in industrial processesExpert Syst Appl20166313414410.1016/j.eswa.2016.06.035 X. Qiu, L. Zhang, Y. Ren, P. N. Suganthan, and G. Amaratunga (2014) Ensemble deep learning for regression and time series forecasting, in Computational Intelligence in Ensemble Learning, pp. 1–6 Z. Liu and M. Hauskrecht (2016) Learning adaptive forecasting models from irregularly sampled multivariate clinical data, in Thirtieth AAAI Conference on Artificial Intelligence, pp. 1273–1279 BifetAHolmesGKirkbyRPfahringerBMoa: massive online analysisJ Mach Learn Res20101116011604 YeePHaykinSA dynamic regularized radial basis function network for nonlinear, nonstationary time series predictionIEEE Trans Signal Process1999472503252117350700979.9402510.1109/78.782193 W. Hong, L. Lei, and F. Wei (2016) Time series prediction based on ensemble fuzzy extreme learning machine, in IEEE International Conference on Information & Automation, pp. 2001–2005 YeRDaiQA novel transfer learning framework for time series forecastingKnowl-Based Syst2018156749910.1016/j.knosys.2018.05.021 Zhou ZH, Wu J, Jiang Y (2001) Genetic algorithm based selective neural network ensemble. Int Joint Conf Artif Intell:797–802 J. O. Gama, R. SebastiÃ£o, and P. P. Rodrigues (2009) Issues in evaluation of stream learning algorithms, in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 329–338 YeYSquartiniSPiazzaFOnline sequential extreme learning machine in nonstationary environmentsNeurocomputing20131169410110.1016/j.neucom.2011.12.064 CruzRMSabourinRCavalcantiGDRenTIMETA-DES: a dynamic ensemble selection framework using META-learningPattern Recogn2015481925193510.1016/j.patcog.2014.12.003 HeHChenSLiKXuXIncremental learning from stream dataIEEE Trans Neural Netw2011221901191410.1109/TNN.2011.2169087 J. Villarreal and P. Baffes, "Time series prediction using neural networks," 1993 SoaresECostaPCostaBLeiteDEnsemble of evolving data clouds and fuzzy models for weather time series predictionAppl Soft Comput20186444545310.1016/j.asoc.2017.12.032 D. Sotiropoulos, A. Kostopoulos, and T. Grapsa (2002) A spectral version of Perry’s conjugate gradient method for neural network training, in Proceedings of 4th GRACM Congress on Computational Mechanics, pp. 291–298 A. Venkatraman, M. Hebert, and J. A. Bagnell (2015) Improving multi-step prediction of learned time series models," in Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 3024–3030 SvarerCHansenLKLarsenJOn Design And Evaluation Of Tapped-Delay Neural-Network ArchitecturesIEEE Int Conf Neural Netw19931–3465110.1109/ICNN.1993.298533 D. Kangin and P. Angelov (2015) Evolving clustering, classification and regression with TEDA, in 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 R. Senanayake, S. O'Callaghan, and F. Ramos (2016) Predicting spatio-temporal propagation of seasonal influenza using variational Gaussian process regression," in Thirtieth AAAI Conference on Artificial Intelligence, pp. 3901–3907 S. Dasgupta and T. Osogami (2017) Nonlinear dynamic Boltzmann machines for time-series prediction, in Thirty-First AAAI Conference on Artificial Intelligence ElmanJLFinding structure in timeCogn Sci19901417921110.1207/s15516709cog1402_1 DielemanSWillettKWDambreJRotation-invariant convolutional neural networks for galaxy morphology predictionMon Not R Astron Soc20154501441145910.1093/mnras/stv632 WangXHanMOnline sequential extreme learning machine with kernels for nonstationary time series predictionNeurocomputing2014145909710.1016/j.neucom.2014.05.068 PolikarRUpdaLUpdaSSHonavarVLearn++: An incremental learning algorithm for supervised neural networksIEEE Trans Syst Man Cybernetics, Part C (Applications Rev)20013149750810.1109/5326.983933 HuangGBZhouHDingXZhangRExtreme learning machine for regression and multiclass classificationIEEE Trans Syst Man Cybernetics Part B20124251352910.1109/TSMCB.2011.2168604 SchusterMPaliwalKKBidirectional recurrent neural networksIEEE Trans Signal Process1997452673268110.1109/78.650093 MuhlbaierMDTopalisAPolikarRLearn++.NC: combining Ensemble of Classifiers with Dynamically Weighted Consult-and-Vote for efficient incremental learning of new classesIEEE Trans Neural Netw20082015216810.1109/TNN.2008.2008326 YangYCheJLiYZhaoYZhuSAn incremental electric load forecasting model based on support vector regressionEnergy201611379680810.1016/j.energy.2016.07.092 HochreiterSSchmidhuberJRLong short-term memoryNeural Computation199791735178010.1162/neco.1997.9.8.1735 ParzenEOn estimation of a probability density function and modeAnn Math Stat196233106510761432820116.1130210.1214/aoms/1177704472 CastilloOMelinPSimulation and forecasting complex economic time series using neural networks and fuzzy logicIEEE Int Conf Syst2001318051810 ZhangWXuAPingDGaoMAn improved kernel-based incremental extreme learning machine with fixed budget for nonstationary time series predictionNeural Comput & Applic20193163765210.1007/s00521-017-3096-3 LiJDaiQYeRA novel double incremental learning algorithm for time series predictionNeural Comput & Applic2019316055607710.1007/s00521-018-3434-0 WangLZengYChenTBack propagation neural network with adaptive differential evolution algorithm for time series forecastingExpert Syst Appl20154285586310.1016/j.eswa.2014.08.018 ChandraRCompetition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series predictionIEEE Trans Neural N Learning Syst20152631233136345326210.1109/TNNLS.2015.2404823 YaoCDaiQSongGSeveral novel dynamic ensemble selection algorithms for time series predictionNeural Process Lett2019501789182910.1007/s11063-018-9957-7 ZhaiJHXuHYWangXZDynamic ensemble extreme learning machine based on sample entropySoft Comput2012161493150210.1007/s00500-012-0824-6 C Svarer (1802_CR52) 1993; 1–3 1802_CR1 H He (1802_CR46) 2011; 22 R Chandra (1802_CR9) 2015; 26 T Zhou (1802_CR50) 2016; 105 O Castillo (1802_CR8) 2001; 3 S Dieleman (1802_CR10) 2015; 450 CG Bezerra (1802_CR53) 2016; 63 L Wang (1802_CR12) 2015; 42 ZH Zhou (1802_CR45) 2002; 137 E Soares (1802_CR51) 2018; 64 1802_CR7 1802_CR39 T Woloszynski (1802_CR36) 2011; 44 J Li (1802_CR24) 2019; 31 L Lin (1802_CR22) 2017; 83 JL Elman (1802_CR27) 1990; 14 Y Yang (1802_CR35) 2016; 113 A Bifet (1802_CR48) 2010; 11 1802_CR20 1802_CR23 1802_CR21 YV Bodyanskiy (1802_CR2) 2020; 945 JH Zhai (1802_CR37) 2012; 16 1802_CR28 JS Lim (1802_CR3) 2013; 22 1802_CR26 SF Crone (1802_CR6) 2011; 27 GB Huang (1802_CR15) 2005; 2 MD Muhlbaier (1802_CR33) 2008; 20 GE Hinton (1802_CR14) 2012; 4 GB Huang (1802_CR17) 2012; 42 E Parzen (1802_CR25) 1962; 33 R Ye (1802_CR19) 2018; 156 R Polikar (1802_CR32) 2001; 31 F Gaxiola (1802_CR11) 2015; 325 1802_CR13 1802_CR54 W Zhang (1802_CR34) 2019; 31 Y Ye (1802_CR4) 2013; 116 X Wang (1802_CR18) 2014; 145 GB Huang (1802_CR16) 2006; 70 M Schuster (1802_CR29) 1997; 45 S Hochreiter (1802_CR30) 1997; 9 1802_CR41 1802_CR42 1802_CR40 C Yao (1802_CR55) 2019; 50 1802_CR43 1802_CR44 P Yee (1802_CR5) 1999; 47 1802_CR49 1802_CR47 N-Y Liang (1802_CR31) 2006; 17 RM Cruz (1802_CR38) 2015; 48
References_xml	– reference: YangYCheJLiYZhaoYZhuSAn incremental electric load forecasting model based on support vector regressionEnergy201611379680810.1016/j.energy.2016.07.092 – reference: SoaresECostaPCostaBLeiteDEnsemble of evolving data clouds and fuzzy models for weather time series predictionAppl Soft Comput20186444545310.1016/j.asoc.2017.12.032 – reference: ZhaiJHXuHYWangXZDynamic ensemble extreme learning machine based on sample entropySoft Comput2012161493150210.1007/s00500-012-0824-6 – reference: BodyanskiyYVTyshchenkoOKA hybrid Cascade neural network with ensembles of extended neo-fuzzy neurons and its deep learningInf Technol Syst Res Comput Phys202094516417410.1007/978-3-030-18058-4_13 – reference: CroneSFHibonMNikolopoulosKAdvances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series predictionInt J Forecast20112763566010.1016/j.ijforecast.2011.04.001 – reference: HintonGESrivastavaNKrizhevskyASutskeverISalakhutdinovRRImproving neural networks by preventing co-adaptation of feature detectorsComput Sci20124212223 – reference: BifetAHolmesGKirkbyRPfahringerBMoa: massive online analysisJ Mach Learn Res20101116011604 – reference: SvarerCHansenLKLarsenJOn Design And Evaluation Of Tapped-Delay Neural-Network ArchitecturesIEEE Int Conf Neural Netw19931–3465110.1109/ICNN.1993.298533 – reference: DielemanSWillettKWDambreJRotation-invariant convolutional neural networks for galaxy morphology predictionMon Not R Astron Soc20154501441145910.1093/mnras/stv632 – reference: ZhangWXuAPingDGaoMAn improved kernel-based incremental extreme learning machine with fixed budget for nonstationary time series predictionNeural Comput & Applic20193163765210.1007/s00521-017-3096-3 – reference: YeePHaykinSA dynamic regularized radial basis function network for nonlinear, nonstationary time series predictionIEEE Trans Signal Process1999472503252117350700979.9402510.1109/78.782193 – reference: H. Yao, F. Wu, J. Ke, X. Tang, Y. Jia, S. Lu, et al.(2018) Deep multi-view spatial-temporal network for taxi demand prediction," in Thirty-Second AAAI Conference on Artificial Intelligence – reference: S. Dasgupta and T. Osogami (2017) Nonlinear dynamic Boltzmann machines for time-series prediction, in Thirty-First AAAI Conference on Artificial Intelligence – reference: Z. Liu and M. Hauskrecht (2016) Learning adaptive forecasting models from irregularly sampled multivariate clinical data, in Thirtieth AAAI Conference on Artificial Intelligence, pp. 1273–1279 – reference: Zhou ZH, Wu J, Jiang Y (2001) Genetic algorithm based selective neural network ensemble. Int Joint Conf Artif Intell:797–802 – reference: D. Kangin and P. Angelov (2015) Evolving clustering, classification and regression with TEDA, in 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 – reference: HuangGBZhouHDingXZhangRExtreme learning machine for regression and multiclass classificationIEEE Trans Syst Man Cybernetics Part B20124251352910.1109/TSMCB.2011.2168604 – reference: ZhouTGaoSWangJChuCTodoYTangZFinancial time series prediction using a dendritic neuron modelKnowl-Based Syst201610521422410.1016/j.knosys.2016.05.031 – reference: A. Venkatraman, M. Hebert, and J. A. Bagnell (2015) Improving multi-step prediction of learned time series models," in Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 3024–3030 – reference: M. C. A. Neto, G. D. C. Cavalcanti, and I. R. Tsang, "Financial time series prediction using exogenous series and combined neural networks," in International Joint Conference on Neural Networks, pp. 2578–2585, 2009 – reference: CastilloOMelinPSimulation and forecasting complex economic time series using neural networks and fuzzy logicIEEE Int Conf Syst2001318051810 – reference: HeHChenSLiKXuXIncremental learning from stream dataIEEE Trans Neural Netw2011221901191410.1109/TNN.2011.2169087 – reference: ZhouZHWuJTangWEnsembling neural networks: many could be better than allArtif Intell200213723926319064770995.6807710.1016/S0004-3702(02)00190-X – reference: E. Ley and M. F. Steel (1993) Bayesian econometrics: Conjugate analysis and rejection sampling, in Economic and Financial Modeling with Mathematica®, ed: Springer, pp. 344–367 – reference: X. Qiu, L. Zhang, Y. Ren, P. N. Suganthan, and G. Amaratunga (2014) Ensemble deep learning for regression and time series forecasting, in Computational Intelligence in Ensemble Learning, pp. 1–6 – reference: SchusterMPaliwalKKBidirectional recurrent neural networksIEEE Trans Signal Process1997452673268110.1109/78.650093 – reference: Carlo E. Bonferroni, "Il calcolo delle assicurazioni su gruppi di teste," In Studi in Onore del Professore Salvatore Ortu Carboni, Rome: Italy, pp. 13–60, 1935 – reference: MuhlbaierMDTopalisAPolikarRLearn++.NC: combining Ensemble of Classifiers with Dynamically Weighted Consult-and-Vote for efficient incremental learning of new classesIEEE Trans Neural Netw20082015216810.1109/TNN.2008.2008326 – reference: CruzRMSabourinRCavalcantiGDRenTIMETA-DES: a dynamic ensemble selection framework using META-learningPattern Recogn2015481925193510.1016/j.patcog.2014.12.003 – reference: YeYSquartiniSPiazzaFOnline sequential extreme learning machine in nonstationary environmentsNeurocomputing20131169410110.1016/j.neucom.2011.12.064 – reference: J. O. Gama, R. SebastiÃ£o, and P. P. Rodrigues (2009) Issues in evaluation of stream learning algorithms, in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 329–338 – reference: M. I. Jordan, "Serial order: A parallel distributed processing approach," in Advances in Psychology. vol. 121, ed: Elsevier, 1997, pp. 471–495 – reference: HochreiterSSchmidhuberJRLong short-term memoryNeural Computation199791735178010.1162/neco.1997.9.8.1735 – reference: J. Villarreal and P. Baffes, "Time series prediction using neural networks," 1993 – reference: HuangGBZhuQYSiewCKExtreme learning machine: theory and applicationsNeurocomputing20067048950110.1016/j.neucom.2005.12.126 – reference: YeRDaiQA novel transfer learning framework for time series forecastingKnowl-Based Syst2018156749910.1016/j.knosys.2018.05.021 – reference: D. Sotiropoulos, A. Kostopoulos, and T. Grapsa (2002) A spectral version of Perry’s conjugate gradient method for neural network training, in Proceedings of 4th GRACM Congress on Computational Mechanics, pp. 291–298 – reference: HuangGBZhuQYSiewCKExtreme learning machine: a new learning scheme of feedforward neural networksIEEE Int Joint Confer Neural Networks20052985990 – reference: ChandraRCompetition and collaboration in cooperative coevolution of Elman recurrent neural networks for time-series predictionIEEE Trans Neural N Learning Syst20152631233136345326210.1109/TNNLS.2015.2404823 – reference: W. Hong, L. Lei, and F. Wei (2016) Time series prediction based on ensemble fuzzy extreme learning machine, in IEEE International Conference on Information & Automation, pp. 2001–2005 – reference: R. Senanayake, S. O'Callaghan, and F. Ramos (2016) Predicting spatio-temporal propagation of seasonal influenza using variational Gaussian process regression," in Thirtieth AAAI Conference on Artificial Intelligence, pp. 3901–3907 – reference: LimJSLeeSPangHSLow complexity adaptive forgetting factor for online sequential extreme learning machine (OS-ELM) for application to nonstationary system estimationsNeural Comput Applic20132256957610.1007/s00521-012-0873-x – reference: LiangN-YHuangG-BSaratchandranPSundararajanNA fast and accurate online sequential learning algorithm for feedforward networksIEEE Trans Neural Netw2006171411142310.1109/TNN.2006.880583 – reference: ElmanJLFinding structure in timeCogn Sci19901417921110.1207/s15516709cog1402_1 – reference: PolikarRUpdaLUpdaSSHonavarVLearn++: An incremental learning algorithm for supervised neural networksIEEE Trans Syst Man Cybernetics, Part C (Applications Rev)20013149750810.1109/5326.983933 – reference: GaxiolaFMelinPValdezFCastilloOGeneralized type-2 fuzzy weight adjustment for backpropagation neural networks in time series predictionInf Sci201532515917433922961328.6816410.1016/j.ins.2015.07.020 – reference: YaoCDaiQSongGSeveral novel dynamic ensemble selection algorithms for time series predictionNeural Process Lett2019501789182910.1007/s11063-018-9957-7 – reference: WangXHanMOnline sequential extreme learning machine with kernels for nonstationary time series predictionNeurocomputing2014145909710.1016/j.neucom.2014.05.068 – reference: WoloszynskiTKurzynskiMA probabilistic model of classifier competence for dynamic ensemble selectionPattern Recogn201144265626681218.6815510.1016/j.patcog.2011.03.020 – reference: ParzenEOn estimation of a probability density function and modeAnn Math Stat196233106510761432820116.1130210.1214/aoms/1177704472 – reference: LiJDaiQYeRA novel double incremental learning algorithm for time series predictionNeural Comput & Applic2019316055607710.1007/s00521-018-3434-0 – reference: WangLZengYChenTBack propagation neural network with adaptive differential evolution algorithm for time series forecastingExpert Syst Appl20154285586310.1016/j.eswa.2014.08.018 – reference: LinLFangWXieXZhongSRandom forests-based extreme learning machine ensemble for multi-regime time series predictionExpert Syst Appl20178316417610.1016/j.eswa.2017.04.013 – reference: BezerraCGCostaBSJGuedesLAAngelovPPAn evolving approach to unsupervised and real-time fault detection in industrial processesExpert Syst Appl20166313414410.1016/j.eswa.2016.06.035 – reference: W. Hong, F. Wei, F. Sun, and X. Qian (2015) An adaptive ensemble model of extreme learning machine for time series prediction," in International Computer Conference on Wavelet Active Media Technology & Information Processing, pp. 80–85 – volume: 113 start-page: 796 year: 2016 ident: 1802_CR35 publication-title: Energy doi: 10.1016/j.energy.2016.07.092 – volume: 945 start-page: 164 year: 2020 ident: 1802_CR2 publication-title: Inf Technol Syst Res Comput Phys doi: 10.1007/978-3-030-18058-4_13 – volume: 31 start-page: 6055 year: 2019 ident: 1802_CR24 publication-title: Neural Comput & Applic doi: 10.1007/s00521-018-3434-0 – ident: 1802_CR39 doi: 10.1609/aaai.v32i1.11836 – volume: 2 start-page: 985 year: 2005 ident: 1802_CR15 publication-title: IEEE Int Joint Confer Neural Networks – volume: 83 start-page: 164 year: 2017 ident: 1802_CR22 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2017.04.013 – ident: 1802_CR49 – ident: 1802_CR41 – ident: 1802_CR54 doi: 10.1109/IJCNN.2015.7280528 – volume: 42 start-page: 855 year: 2015 ident: 1802_CR12 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2014.08.018 – ident: 1802_CR28 doi: 10.1016/S0166-4115(97)80111-2 – volume: 44 start-page: 2656 year: 2011 ident: 1802_CR36 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2011.03.020 – ident: 1802_CR13 – volume: 27 start-page: 635 year: 2011 ident: 1802_CR6 publication-title: Int J Forecast doi: 10.1016/j.ijforecast.2011.04.001 – volume: 31 start-page: 637 year: 2019 ident: 1802_CR34 publication-title: Neural Comput & Applic doi: 10.1007/s00521-017-3096-3 – volume: 450 start-page: 1441 year: 2015 ident: 1802_CR10 publication-title: Mon Not R Astron Soc doi: 10.1093/mnras/stv632 – ident: 1802_CR47 doi: 10.1145/1557019.1557060 – volume: 48 start-page: 1925 year: 2015 ident: 1802_CR38 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2014.12.003 – volume: 145 start-page: 90 year: 2014 ident: 1802_CR18 publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.05.068 – volume: 26 start-page: 3123 year: 2015 ident: 1802_CR9 publication-title: IEEE Trans Neural N Learning Syst doi: 10.1109/TNNLS.2015.2404823 – volume: 11 start-page: 1601 year: 2010 ident: 1802_CR48 publication-title: J Mach Learn Res – volume: 70 start-page: 489 year: 2006 ident: 1802_CR16 publication-title: Neurocomputing doi: 10.1016/j.neucom.2005.12.126 – volume: 1–3 start-page: 46 year: 1993 ident: 1802_CR52 publication-title: IEEE Int Conf Neural Netw doi: 10.1109/ICNN.1993.298533 – volume: 325 start-page: 159 year: 2015 ident: 1802_CR11 publication-title: Inf Sci doi: 10.1016/j.ins.2015.07.020 – volume: 4 start-page: 212 year: 2012 ident: 1802_CR14 publication-title: Comput Sci – ident: 1802_CR1 – volume: 137 start-page: 239 year: 2002 ident: 1802_CR45 publication-title: Artif Intell doi: 10.1016/S0004-3702(02)00190-X – volume: 17 start-page: 1411 year: 2006 ident: 1802_CR31 publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2006.880583 – ident: 1802_CR21 – volume: 105 start-page: 214 year: 2016 ident: 1802_CR50 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2016.05.031 – volume: 64 start-page: 445 year: 2018 ident: 1802_CR51 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2017.12.032 – volume: 9 start-page: 1735 year: 1997 ident: 1802_CR30 publication-title: Neural Computation doi: 10.1162/neco.1997.9.8.1735 – ident: 1802_CR42 doi: 10.1609/aaai.v31i1.10806 – volume: 20 start-page: 152 year: 2008 ident: 1802_CR33 publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2008.2008326 – volume: 22 start-page: 569 year: 2013 ident: 1802_CR3 publication-title: Neural Comput Applic doi: 10.1007/s00521-012-0873-x – volume: 156 start-page: 74 year: 2018 ident: 1802_CR19 publication-title: Knowl-Based Syst doi: 10.1016/j.knosys.2018.05.021 – ident: 1802_CR7 – volume: 63 start-page: 134 year: 2016 ident: 1802_CR53 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2016.06.035 – volume: 14 start-page: 179 year: 1990 ident: 1802_CR27 publication-title: Cogn Sci doi: 10.1207/s15516709cog1402_1 – ident: 1802_CR43 – volume: 31 start-page: 497 year: 2001 ident: 1802_CR32 publication-title: IEEE Trans Syst Man Cybernetics, Part C (Applications Rev) doi: 10.1109/5326.983933 – ident: 1802_CR20 – volume: 42 start-page: 513 year: 2012 ident: 1802_CR17 publication-title: IEEE Trans Syst Man Cybernetics Part B doi: 10.1109/TSMCB.2011.2168604 – volume: 16 start-page: 1493 year: 2012 ident: 1802_CR37 publication-title: Soft Comput doi: 10.1007/s00500-012-0824-6 – volume: 47 start-page: 2503 year: 1999 ident: 1802_CR5 publication-title: IEEE Trans Signal Process doi: 10.1109/78.782193 – ident: 1802_CR23 doi: 10.1109/CIEL.2014.7015739 – volume: 45 start-page: 2673 year: 1997 ident: 1802_CR29 publication-title: IEEE Trans Signal Process doi: 10.1109/78.650093 – volume: 22 start-page: 1901 year: 2011 ident: 1802_CR46 publication-title: IEEE Trans Neural Netw doi: 10.1109/TNN.2011.2169087 – volume: 3 start-page: 1805 year: 2001 ident: 1802_CR8 publication-title: IEEE Int Conf Syst – volume: 33 start-page: 1065 year: 1962 ident: 1802_CR25 publication-title: Ann Math Stat doi: 10.1214/aoms/1177704472 – ident: 1802_CR44 – ident: 1802_CR40 – ident: 1802_CR26 doi: 10.1007/978-1-4757-2281-9_15 – volume: 116 start-page: 94 year: 2013 ident: 1802_CR4 publication-title: Neurocomputing doi: 10.1016/j.neucom.2011.12.064 – volume: 50 start-page: 1789 year: 2019 ident: 1802_CR55 publication-title: Neural Process Lett doi: 10.1007/s11063-018-9957-7
SSID	ssj0003301
Score	2.314569
Snippet	Ensemble pruning can effectively overcome several shortcomings of the classical ensemble learning paradigm, such as the relatively high time and space...
SourceID	proquest crossref springer
SourceType	Aggregation Database Enrichment Source Index Database Publisher
StartPage	617
SubjectTerms	Algorithms Artificial Intelligence Computer Science Density distribution Kernels Machine learning Machines Manufacturing Mechanical Engineering Performance prediction Processes Time series Traveling salesman problem
SummonAdditionalLinks	– databaseName: Springer Journals New Starts & Take-Overs Collection dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1bS8MwFA46ffDFecXplDyIL67QdlmS-iY6EZQxvLG3kiapG27daDth_8Mf7Ekvm4oK-lTaJuGQk5Ock3P5EDpWggWhTT0QJOGCgcK05SnHJPiEpCVCBWwPMrAJ1unwXs_rFklhSRntXroks536Q7IbMeE9rgmk4iDHZBmtwHHHDWDD3f3TfP8FCz3DyQPLwqLU6xWpMt-P8fk4WuiYX9yi2WlzVf0fnRtovdAu8Xm-HDbRko62ULVEbsCFIG-jt3aUdG8u2yewPdzC8wwL3J-Z5C1swOaxWZc6wZPYuHEM67AYPo_jQdof4bLABNCEVY5nj8EW1qNgqKHH1Fy0NKCVzG8egZwCmQK-ikjhFx1HeoiViZxPZ9hU-cjTJ3fQ41X74eLaKvAZLAlGW2qFjtJMujLgUkpCiGoxCRpLwAPiKg7vTHLNudN0qJCBcm0hiGcrpgilkju6uYsq0TjSewjbyqYhJ67thi2iQUkSoPgQFiihuS29Zg05JZt8WRQvNxgaQ39RdtlMuw_T7mfT7pMaOp33meSlO35tXS-57xdinPhAAgWVC3SiGmqU3F78_nm0_b81P0BrromVyaLB66iSxlN9iFblazpI4qNseb8DXjX1UQ priority: 102 providerName: Springer Nature
Title	EnsPKDE&IncLKDE: a hybrid time series prediction algorithm integrating dynamic ensemble pruning, incremental learning, and kernel density estimation
URI	https://link.springer.com/article/10.1007/s10489-020-01802-4 https://www.proquest.com/docview/2476741129
Volume	51
WOSCitedRecordID	wos000561725100002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAVX databaseName: Springer Journals New Starts & Take-Overs Collection customDbUrl: eissn: 1573-7497 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0003301 issn: 0924-669X databaseCode: RSV dateStart: 19970101 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NTtwwEB7xd-gFCm3FFrryoeJSoiZeb-xwQRQWIdGuVkDbbS-RYztQdQnbTUDiPXjgziQOq1Yql14cJbEdR2OPZ8Yz8wG8tVpmeRgnuJA0RwVFuiCxEQX45KKvc4tkz2qwCTkcqvE4GXmDW-ndKlueWDNqe2PIRv6eC0o7Q9LB_vRXQKhRdLrqITQWYZmyJES16975IydGXb1GzEMdI4jjZOyDZnzonCBnIU5uWQq5gvhzY5pLm38dkNb7zvHa_474Oax6iZMdNFNkHRZcsQFrLZoD84v7BTwMinJ0ejTYQZbxEa97TLOrewroYgRAz2iuupJNZ3S0Q-RkenKJ36uurlmbdAL_gNkG456hfuyus4nDFrdkfNnFWqaxRuJwPFoFPtWFZT_drHATZsmbvrpnlPmjCal8CZ-PBxeHJ4HHbAgMKnJVkEfWScNNpowxQgjblwalmExlgluF99Iop1TUi2JtMstDrUUSWmlFHBsVud4rWCpuCrcJLLRhnCvBQ573hUPBSaMwJGRmtVOhSXodiFqCpcYnNCdcjUk6T8VMRE6RyGlN5FR04N1jm2mTzuPJ2tstZVO_tMt0TtYO7LZzY_763729frq3LXjGyV-m9gjfhqVqduvewIq5q36Usy4syq_furD8YTAcneHdqQyw_BQeduspj-Wo_x3Ls_MvvwGxpgXr
linkProvider	ProQuest
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VFgkutDwqFkrxAbjQqInjjR0khKp2q1a7rHoo0t6CYzvtqrvpdpOC9n_wO_iNzOTRVZHorQdOUV5WYn8znrFn5gN4Z7VMMz-KUZA0RwdFOi-2ASX4ZKKrM4vDnlZkE3I4VKNRfLICv9tcGAqrbHVipajtpaE18l0uqOwMWQdfZlcesUbR7mpLoVHDou8WP9FlKz4fH-D4vuf8sHe6f-Q1rAKeQVej9LLAOmm4SZUxRghhu9LgPJuqVHCr8Fwa5ZQKwiDSJrXc11rEvpVWRJFRgQux3QewJkIlSa760rvR_GFY0S376NN4URSPmiSdJlVPUHASpzAwhVpI3J4Il9btXxuy1Tx3uP6_9dAGPGksarZXi8BTWHH5M1hv2SpYo7yew69eXpz0D3ofUCUO8PiJaXa-oIQ1Vo6njpEsuoLN5rR1RXBlenKG_1eeT1lbVAN7jNlFrqdjw9D_d9N04vCNa1pc2sGnTL3aip_TsHHgVZ1bduHmuZswS9kC5YJRZZM6ZfQFfLuXvtmE1fwydy-B-daPMiW4z7OucGgYajT2hEytdso3cdiBoAVIYpqC7cQbMkmWpaYJVAmCKqlAlYgOfLx5Z1aXK7nz6a0WSUmjuopkCaMO7LRYXN7-d2uv7m7tLTw6Ov06SAbHw_5reMwpNqiKft-C1XJ-7d7AQ_OjHBfz7UqoGHy_b4z-AZ8FXX4
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3Nb9MwFH8aAyEujE9RGOADcGHREseNHSSEEG3F1KnqAaSKS3Bsh1W0WWkyUP8P_hr-Ot5LnFUgsdsOnKIktpXYv_dlvw-AZ1bLvAiTFAlJczRQpAtSG1GATyH6urC47HlTbEJOJmo2S6c78KuLhSG3yo4nNozanhraIz_kgtLOkHZwWHi3iOlg9Gb1LaAKUnTS2pXTaCEydpsfaL5Vr48GuNbPOR8NP7x7H_gKA4FBs6MOisg6abjJlTFGCGH70qDMzVUuuFV4L41ySkVxlGiTWx5qLdLQSiuSxKjIxTjuFbgq0cYkd8Jp_9O5FIjjpvRyiPZNkCTpzAfs-LA9QY5KnFzCFHIk8adQ3Gq6fx3ONjJvtPc_z9YtuOk1bfa2JY3bsOPKO7DXVbFgnqndhZ_DspqOB8MXyCqP8fqKaXayoUA2Vs-XjhGNuoqt1nSkRTBmevEF_68-WbIu2QbOHrObUi_nhrmycst84bDHGW06HWAr0-7C4uf4Kh34VJeWfXXr0i2YpSiCesMo40kbSnoPPl7K3NyH3fK0dA-AhTZMCiV4yIu-cKgwalQChcytdio0adyDqANLZnwid6onssi2KagJYBkCLGsAlokevDzvs2rTmFzYer9DVeZZWpVtIdWDgw6X29f_Hu3hxaM9hesIzez4aDJ-BDc4uQw1TvH7sFuvz9xjuGa-1_Nq_aShLwafLxuivwGkVWai
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=EnsPKDE%26IncLKDE%3A+a+hybrid+time+series+prediction+algorithm+integrating+dynamic+ensemble+pruning%2C+incremental+learning%2C+and+kernel+density+estimation&rft.jtitle=Applied+intelligence+%28Dordrecht%2C+Netherlands%29&rft.au=Zhu%2C+Gangliang&rft.au=Dai%2C+Qun&rft.date=2021-02-01&rft.issn=0924-669X&rft.eissn=1573-7497&rft.volume=51&rft.issue=2&rft.spage=617&rft.epage=645&rft_id=info:doi/10.1007%2Fs10489-020-01802-4&rft.externalDBID=n%2Fa&rft.externalDocID=10_1007_s10489_020_01802_4
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0924-669X&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0924-669X&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0924-669X&client=summon