Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for...
Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns
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Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Language
English
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Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Contents
In this study, we present the EEG-GCN, a novel hybrid model for the prediction of time series data, adept at addressing the inherent challenges posed by the data's complex, non-linear, and periodic nature, as well as the noise that frequently accompanies it. This model synergizes signal decomposition techniques with a graph convolutional neural net...
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Full title
Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns
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TN_cdi_doaj_primary_oai_doaj_org_article_bbb0020e4d1441b1ae6dabd92b0432d3
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_bbb0020e4d1441b1ae6dabd92b0432d3
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ISSN
2192-113X
E-ISSN
2192-113X
DOI
10.1186/s13677-023-00560-1
