In order to solve the problem that the ARIMA model cannot well fit the prediction of time series with high dimension and high noise, this paper proposes a method based on the combination of hybrid reduction and BP neural network. Taking the stock price as an example, the proposed method takes the intraday price as auxiliary information and uses PCA and KPCA to extract linear and nonlinear features of it respectively, and the dimensionally reduced features are then used as the input variable. BP neural network model was used to fit the residual error between the real value and the predicted value of the ARIMA model. Finally, add up the closing price prediction value obtained by the ARIMA model and the residual error prediction value obtained by BP neural network model for the final closing price prediction value. The empirical results show that compared with the ARIMA model, the proposed method has better prediction performance and fitting accuracy, and has certain robustness. This method can also be extended to other practical problems such as average temperature prediction and port ship flow prediction.

Longhui Mo. An Improved ARIMA Method Based on Hybrid Dimension Reduction and BP Neural Network. Academic Journal of Computing & Information Science (2022), Vol. 5, Issue 10: 41-47. https://doi.org/10.25236/AJCIS.2022.051007.

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