Key result
EEG-GCN model improves time series forecasting accuracy by >~60% over baseline GCN.
Why the study?
Time series data prediction is challenged by complex, non-linear, and periodic patterns as well as frequent noise interference.
The proposed EEG-GCN hybrid model significantly improves predictive accuracy for non-stationary time series data compared to baseline GCN models.
May enhance research forecasting of non-stationary signals; leaves open prospective validation before any clinical use in cardiology.
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 decomposition using both Ensemble Empirical Mode Decomposition (EEMD) and GRU. This two-pronged decomposition process effectively eliminates noise interference and distills the complex signal into more tractable sub-signals. These sub-signals facilitate a more straightforward feature analysis and learning process. To capitalize on the decomposed data, a graph convolutional neural network (GCN) is employed to discern the intricate feature interplay within the sub-signals and to map the interdependencies among the data points. The predictive model then synthesizes the weighted outputs of the GCN to yield the final forecast. A key component of our approach is the integration of a Gated Recurrent Unit (GRU) with EEMD within the GCN framework, referred to as EEMD-GRU-GCN. This combination leverages the strengths of GRU in capturing temporal dependencies and the EEMD's capability in handling non-stationary data, thereby enriching the feature set available for the GCN and enhancing the overall predictive accuracy and stability of the model. Empirical evaluations demonstrate that the EEG-GCN model achieves superior performance metrics. Compared to the baseline GCN model, EEG-GCN shows an average R2 improvement of 60% to 90%, outperforming the other methods. These results substantiate the advanced predictive capability of our proposed model, underscoring its potential for robust and accurate time series forecasting.
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Han et al. (2024) studied Time series forecasting. EEG-GCN model vs. GCN, EEMD-GCN, CEEMDAN-GCN, EMD-CEEMDAN-GCN was evaluated on Prediction accuracy (R2, MAE, MSE, MAPE). The EEG-GCN model achieved superior performance metrics for time series forecasting, showing an average R2 improvement of 60% to 90% compared to the baseline GCN model.
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