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dc.contributor.authorLi, Chongen
dc.contributor.authorYang, Yingjieen
dc.contributor.authorLiu, Sifengen
dc.date.accessioned2018-11-06T12:06:08Z
dc.date.available2018-11-06T12:06:08Z
dc.date.issued2018-08-23
dc.identifier.citationLi, C., Yang, Y. and Liu, S. (2019) Comparative analysis of properties of weakening buffer operators in time series prediction models. Communications in Nonlinear Science and Numerical Simulation, 68, pp.257-285.en
dc.identifier.issn1007-5704
dc.identifier.urihttp://hdl.handle.net/2086/17069
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractReducing the negative influence of stochastic disturbances in sample data has always been a difficult problem in time series analysis. In this paper, three new fractional weakening buffer operators are proposed, and then some desirable properties of these proposed se- quence operators are investigated. Their potential effect in smoothing unexpected distur- bances while maintaining the normal trend in sample series is analyzed and compared with other widely used sequence operators in time series modeling. Results of theoretical and empirical research show that the proposed novel fractional weakening buffer oper- ators are effective in improving the development pattern analysis of time series in dis- turbance scenarios, while also avoid too subjectively weighting experimental data from collected samples. The robust of the proposed operator-based prediction algorithm against noise effect is tested in five different types of noise scenarios. Result of empirical study demonstrates that the proposed method improves the series prediction performance and it also improves the robustness of corresponding forecasting algorithms. These unique prop- erties of the proposed weakening buffer operators make them more attractive in time se- ries analysis.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectWeakening buffer operatoren
dc.subjectTime series smoothnessen
dc.subjectData disturbancesen
dc.subjectTrend predictionen
dc.titleComparative analysis of properties of weakening buffer operators in time series prediction modelsen
dc.typeArticleen
dc.identifier.doihttps://doi.org/10.1016/j.cnsns.2018.06.029
dc.researchgroupInstitute of Artificial Intelligence (IAI)en
dc.peerreviewedYesen
dc.funderLeverhulmeen
dc.funderRoyal Societyen
dc.projectidIN-2014-020en
dc.projectidIEC\NSFC\170391en
dc.cclicenceCC-BY-NCen
dc.date.acceptance2018-06-22en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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