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Academic Journal of Computing & Information Science, 2023, 6(7); doi: 10.25236/AJCIS.2023.060713.

Combined CEEMDAN-CNN-BiLSTM-ATT Model for Forex Forecasting


Xin Li, Guoqiang Tang, Yumei Ren, Xuchang Chen

Corresponding Author:
Guoqiang Tang

Faculty of Science, Guilin University of Technology, Guilin, Guangxi Zhuang Autonomous Region, China


With the deepening of economic globalization and the expanding scale of the foreign exchange market, the study of the volatility characteristics and forecasting of foreign exchange has received widespread attention.In this paper, a new forex portfolio forecasting model is proposed, which uses fully integrated empirical modal adaptive noise decomposition (CEEMDAN) to decompose the original forex price into sub-layers of different frequencies; then, convolutional neural network, bi-directional long and short term memory and attention mechanism module (CNN-BiLSTM-Attention) are used to combine the forecasting of each sub-layer; finally, the forecasting results of different frequency sub-layers are summed Reconstructed as the final prediction value. The forex series of USD, EUR, JPY and HKD against CNY were selected for empirical analysis, and based on the results of both evaluation metrics and DM tests, it was concluded that the CEEMDAN-CNN-BiLSTM-Attention model performed better in the forex market and had higher accuracy in exchange rate forecasting compared to the single model and other combined models.


Foreign exchange forecasting; CEEMDAN; CNN-BiLSTM-Attention; DM test

Cite This Paper

Xin Li, Guoqiang Tang, Yumei Ren, Xuchang Chen. Combined CEEMDAN-CNN-BiLSTM-ATT Model for Forex Forecasting. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 7: 88-100. https://doi.org/10.25236/AJCIS.2023.060713.


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