Jan 18, 2024 · 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 ...
Jan 18, 2024 · This combination leverages the strengths of GRU in capturing temporal dependencies and the EEMD's capability in handling non-stationary data, ...
Jan 18, 2024 · 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 ...
This two-pronged decomposition process effectively eliminates noise interference and distills the complex signal into more tractable sub-signals. These sub- ...
Mar 27, 2024 · Correction to: Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non‑stationary data patterns.
Mar 27, 2024 · Correction to: Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non‑stationary data patterns.
Correction to: Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non‑stationary data patterns. Authors ...
Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns. Journal of cloud computing ...
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Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns . Han, Huimin; Neira-Molin ...
Statistics: Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns ...