Advanced series decomposition with a gated recurrent unit and graph convolutional neural network for non-stationary data patterns

In this study, we present the EEG-GCN, a novel hybrid model for the prediction of time series data, adept at address ing the inherent challenges posed by the data’s complex, non-linear, and periodic nature, as well as the noise that fre quently 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 Decomposi tion (EEMD) and GRU. This two-pronged decomposition process efectively 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 fnal 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.

• Autores: Han, Huimin; Neira Molin, Harold; Khan, Asad; Fang, Meie; Mahmoud, Haitham A.; Mahrous Awwad, Emad; Ahmed, Bilal; Yasin Ghadi, Yazeed
• Tipo de recurso: Article of journal
• Fecha de publicación: 2024
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/10681
• Acceso en línea: https://hdl.handle.net/11323/10681; https://repositorio.cuc.edu.co
• Palabra clave: Time series forecasting; EEMD; CEEMDAN; GCN
• Rights: openAccess
• License: Atribución 4.0 Internacional (CC BY 4.0)