2024
DOI: 10.1186/s13677-023-00560-1
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Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns

Huimin Han,
Harold Neira-Molina,
Asad Khan
et al.

Abstract: 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 network (GCN) for enhanced analytical precision. The EEG-GCN approaches time series data as a one-dimensional temporal signal, applying a dual-layered signal decomposit… Show more

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Cited by 4 publications
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“…Refs. [36][37][38] proposed a hybrid deep learning approach integrating GRUs into MG control, enhancing system efficiency and adaptability under dynamic conditions.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Refs. [36][37][38] proposed a hybrid deep learning approach integrating GRUs into MG control, enhancing system efficiency and adaptability under dynamic conditions.…”
Section: Literature Reviewmentioning
confidence: 99%