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This paper presents a novel hybrid deep learning model, CEEMDAN-Informer-LSTM, aimed at improving financial time series forecasting, particularly for the CSI 300 index. The model utilizes complete ensemble empirical mode decomposition of adaptive noise (CEEMDAN) to decompose signals into high and low-frequency components, leveraging Informer for high-frequency and LSTM for low-frequency predictions. Empirical results demonstrate that the proposed model outperforms traditional and other advanced models in pr

Author

dolawe2521

Language

EN