Please use this identifier to cite or link to this item: https://doi.org/10.23919/SICE.2019.8859841
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dc.titleComparison of Methods for Granger Causality Network Estimation
dc.contributor.authorVenugopal, A
dc.contributor.authorDutta, A
dc.contributor.authorSamavedham, L
dc.contributor.authorKarimi, IA
dc.date.accessioned2020-05-28T00:56:43Z
dc.date.available2020-05-28T00:56:43Z
dc.date.issued2019-09-01
dc.identifier.citationVenugopal, A, Dutta, A, Samavedham, L, Karimi, IA (2019-09-01). Comparison of Methods for Granger Causality Network Estimation. 2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) : 191-196. ScholarBank@NUS Repository. https://doi.org/10.23919/SICE.2019.8859841
dc.identifier.isbn9784907764678
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/168555
dc.description.abstract© 2019 The Society of Instrument and Control Engineers - SICE. Causal relationships among variables serve as a useful tool for augmenting process understanding and to help diagnose the source of abnormalities should they occur in chemical and related process units. Several approaches to decipher the network of relationships from process data have been developed over the years. In this work, we describe an approach for causality determination using Canonical Variate Analysis and compare its results with two other recently proposed approaches that employ Vector Autoregressive (VAR) and Vector Autoregressive Moving Average (VARMA) models. Three simple case studies are presented to compare the efficacy of the approaches.
dc.publisherIEEE
dc.sourceElements
dc.typeConference Paper
dc.date.updated2020-05-27T08:12:35Z
dc.contributor.departmentCHEMICAL & BIOMOLECULAR ENGINEERING
dc.description.doi10.23919/SICE.2019.8859841
dc.description.sourcetitle2019 58th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE)
dc.description.page191-196
dc.published.statePublished
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