Stability of Time Series Models based on Fractional Order Weakening Buffer Operators

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dc.cclicenceCC-BY-NCen
dc.contributor.authorLi, Chong
dc.contributor.authorYang, Yingjie
dc.contributor.authorZhu, Xinping
dc.date.acceptance2023-06-27
dc.date.accessioned2023-09-26T09:20:28Z
dc.date.available2023-09-26T09:20:28Z
dc.date.issued2023-07-17
dc.descriptionopen access article
dc.description.abstractDifferent weakening butter operators in time series model analysis usually result in different model sensitivity, which sometimes affects the effectiveness of relevant operator-based methods. In this paper, the stability of two classic weakening buffer operator-based series models is studied; then a new data preprocessing method based on a novel fractional bidirectional weakening buffer operator is provided, whose effect in improving model stability is tested and utilized in prediction problems. Practical examples are employed to demonstrate the efficiency of the proposed method in improving model stability in noise scenarios. The comparison indicates that the proposed method overcomes the disadvantage of many weakening buffer operators in too subjectively biased weighting the new or the old information in forecasting. These expand the application of the proposed method in time series analysisen
dc.funderNo external funderen
dc.identifier.citationLi, C., Yang, Y. and Zhu, X. (2023) Stability of Time Series Models based on Fractional Order Weakening Buffer Operators. Fractal and Fractional. 7 (7), 554
dc.identifier.doihttps://doi.org/10.3390/fractalfract7070554
dc.identifier.issn2504-3110
dc.identifier.urihttps://hdl.handle.net/2086/23235
dc.language.isoenen
dc.peerreviewedYesen
dc.publisherMDPI
dc.relation.ispartofseries7;7
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en
dc.subjectTime seriesen
dc.subjectsequence operatoren
dc.subjectmodel stabilityen
dc.subjectmodel perturbation analysisen
dc.titleStability of Time Series Models based on Fractional Order Weakening Buffer Operatorsen
dc.typeArticleen

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