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Academic Journal of Computing & Information Science, 2022, 5(9); doi: 10.25236/AJCIS.2022.050913.

A stacked forecasting model-CEEMDAN-BPMs model


Yi Chen, Eva Khong

Corresponding Author:
Yi Chen

Faculty of Finance, City University of Macau, Macau, China


For the predictive effects of individual models vary, unlike conventional implementations, this paper presents a new more comprehensive approach-CEEMDAN-BPMs model, which aim to the goal to the accuracy improvement of stock price forecasting, especially interesting for analysis nonlinear and nonstationary financial time series. This paper introduced a new model, that is BPMs model, furthermore, this new model is applied to financial time series prediction for high frequency data and low frequency data respectively. The key idea on the BPMs model relies on method of weighted linear stacking. Based on this new model with two decomposition method empirical mode decomposition (EMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) are proposed in this paper. The paper employs the indicator mean absolute percentage error(MAPE), mean absolute error(MAE), mean square error(MSE)and correlation coefficient evaluation criterion and empirical results present that forecasting effects of new model CEEMDAN-BPMs is optimal in forecasting.


Forecasting, EMD-BPMs forecasting, CEEMDAN-BPMs forecasting

Cite This Paper

Yi Chen, Eva Khong. A stacked forecasting model-CEEMDAN-BPMs model . Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 9: 82-89. https://doi.org/10.25236/AJCIS.2022.050913.


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