Predicting the Weather: A Partnership of Observation Scientists and Forecasters

Joe, Paul; Sun, Jenny; Yussouf, Nusrat; Goodman, S. J.; Riemer, Michael; Gouda, Krishna Chandra; Golding, Brian; Rogers, Robert F.; Isaac, George A.; Wilson, J.M.; Li, Ping Wah Peter; Wulfmeyer, Volker; Elmore, Kim; Onvlee, Jeanette; Pei, Chong; LaDue, James G.

doi:10.1007/978-3-030-98989-7_7

Cited by 5 publications

(1 citation statement)

References 113 publications

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“…Historically, an analog methodology was initially employed to project wind speed distributions, representing a nascent step in predictive modeling [25]. This was superseded by the advent of time series models, which aimed to forecast wind power several hours ahead, thereby facilitating more agile energy management strategies [26].…”

Section: Traditional Methodsmentioning

confidence: 99%

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

Han,

Neira-Molina,

Khan

et al. 2024

J Cloud Comp

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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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Section: Traditional Methodsmentioning

confidence: 99%