Constructing Spatial Relationship and Temporal Relationship Oriented Composite Fuzzy Cognitive Maps for Multivariate Time Series Forecasting

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dc.authorscopusidWitold Pedrycz / 58861905800
dc.authorwosidWitold Pedrycz / HJZ-2779-2023
dc.contributor.authorOuyang, Chenxi
dc.contributor.authorYang, Fei
dc.contributor.authorYu, Fusheng
dc.contributor.authorPedrycz, Witold
dc.contributor.authorHomenda, Wladyslaw
dc.contributor.authorChang, Jiaqi
dc.contributor.authorHe, Qian
dc.contributor.authorYang, Zonglin
dc.date.accessioned2025-04-18T08:33:32Z
dc.date.available2025-04-18T08:33:32Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractFuzzy cognitive maps (FCMs) are directed graphs with multiple nodes, making them well-suited for addressing multivariate time series (MTS) forecasting problems. When forecasting MTS, it is crucial to treat each vector of the MTS as a whole, considering both the causalities between different variables of the vector at a timepoint (spatial relationship) and the causalities between multiple historical vectors and future vector (temporal relationship). Existing FCM-based MTS forecasting models often fail to treat the vectors as a whole and do not distinctly reflect the temporal relationship and spatial relationship in MTS. To address these limitations, this article introduces the concept of composite FCMs (CFCMs). A CFCM comprises two layers of FCMs: the layer-1 FCM describes the temporal relationship in an MTS, whereas the layer-2 FCM describes the spatial relationship. By embedding the layer-2 FCMs into the nodes of the layer-1 FCM, the relationships within the MTS can be separately reflected while still treating each vector as a whole. In this structure, the nodes of the layer-1 FCM represent historical vectors used to forecast the future vector, and each node of the layer-1 FCM corresponds to a layer-2 FCM whose nodes represent the variables of the vector at a specific historical timepoint in the MTS. Based on the novel CFCM concept, this article proposes a new MTS forecasting model that can distinctly reflect the temporal and spatial relationships in an MTS and utilize multiple historical vectors to forecast the future vector. Experimental results demonstrate the effectiveness of the proposed MTS forecasting model. © 1993-2012 IEEE.
dc.identifier.citationOuyang, C., Yang, F., Yu, F., Pedrycz, W., Homenda, W., Chang, J., ... & Yang, Z. (2024). Constructing Spatial Relationship and Temporal Relationship Oriented Composite Fuzzy Cognitive Maps for Multivariate Time Series Forecasting. IEEE Transactions on Fuzzy Systems.
dc.identifier.doi10.1109/TFUZZ.2024.3395833
dc.identifier.endpage4351
dc.identifier.issn10636706
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85192143598
dc.identifier.scopusqualityQ1
dc.identifier.startpage4338
dc.identifier.urihttp://dx.doi.org/10.1109/TFUZZ.2024.3395833
dc.identifier.urihttps://hdl.handle.net/20.500.12713/6582
dc.identifier.volume32
dc.identifier.wosWOS:001291157800044
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorPedrycz, Witold
dc.institutionauthoridWitold Pedrycz / 0000-0002-9335-9930
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIEEE Transactions on Fuzzy Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectComposite Fuzzy Cognitive Map (CFCM)
dc.subjectSet-valued Time Series
dc.subjectSpatial Relationship
dc.subjectTemporal Relationship
dc.titleConstructing Spatial Relationship and Temporal Relationship Oriented Composite Fuzzy Cognitive Maps for Multivariate Time Series Forecasting
dc.typeArticle

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