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East China Normal University
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Oscillatory-state alignment in AOSNET significantly enhances forecasting accuracy in non-stationary time series, outperforming traditional fixed-template methods.
By explicitly aligning the graph structures of predicted and actual time series correlations, GCGNet achieves superior forecasting accuracy in noisy, real-world datasets compared to methods that model temporal and channel dependencies separately.
