Time series prediction with granular neural networks

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dc.contributor.authorSong, Mingli
dc.contributor.authorLi, Yan
dc.contributor.authorPedrycz, Witold
dc.date.accessioned2024-05-19T14:47:02Z
dc.date.available2024-05-19T14:47:02Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractConventional artificial neural networks are inherently equipped with an ambiguous (uncertain) structure which is hard to be quantified and explained. Time series forecasting using neural networks thus becomes a highly challenging task also due to the fact that time series data are always nonlinear and uncertain (because of some disturbances). Considering this, we propose a granular neural network -based time series prediction model connecting the uncertainty of models and data with the concept of information granularity. We aim to provide an explainable time series prediction model to resist the dis-turbance inner time series data and reduce the vagueness of the model. The functionalities of the granular neural network model are threefold: (1) It reveals the uncertainty of a time series data set through the level of granularity, coverage and specificity and possesses high prediction accuracy; (2) It provides an opti-mized interval output endowed with enough specificity and sufficient coverage and this interval is more robust than a single value; (3) It owns an interpretable and flexible structure that reflects the uncertainty of the initial numeric neural network and the generalization and robustness of connections while being faced with disturbances. The experimental studies elaborate on each function of our model in detail and show that the developed method performed better than the existing approaches present in the literature when experimenting on several time series data sets.(c) 2023 Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipNational Natural Science Foundation of China (NSFC) [61773352]; Fundamental Research Funds for the Central Universitiesen_US
dc.description.sponsorshipSupport from the National Natural Science Foundation of China (NSFC) 61773352 and the Fundamental Research Funds for the Central Universities are gratefully appreciated.en_US
dc.identifier.doi10.1016/j.neucom.2023.126328
dc.identifier.issn0925-2312
dc.identifier.issn1872-8286
dc.identifier.scopus2-s2.0-85159789557en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.neucom.2023.126328
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5639
dc.identifier.volume546en_US
dc.identifier.wosWOS:001010894400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofNeurocomputingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectTime Series Predictionen_US
dc.subjectGranular Neural Networksen_US
dc.subjectCuckoo Searchen_US
dc.subjectUncertaintyen_US
dc.titleTime series prediction with granular neural networksen_US
dc.typeArticleen_US

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