Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model

Shaobo Lu

36 citations0/0 files storedComputational Intelligence and Neuroscience

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Forecasting Techniques and Applications Neural Networks and Applications Stock Market Forecasting Methods

Abstract

Based on the BP neural network and the ARIMA model, this paper predicts the nonlinear residual of GDP and adds the predicted values of the two models to obtain the final predicted value of the model. First, the focus is on the ARMA model in the univariate time series. However, in real life, forecasts are often affected by many factors, so the following introduces the ARIMAX model in the multivariate time series. In the prediction process, the network structure and various parameters of the neural network are not given in a systematic way, so the operation of the neural network is affected by many factors. Each forecasting method has its scope of application and also has its own weaknesses caused by the characteristics of its own model. Secondly, this paper proposes an effective combination method according to the GDP characteristics and builds an improved algorithm BP neural network price prediction model, the research on the combination of GDP prediction model is currently mostly focused on the weighted form, and this article proposes another combination, namely, error correction. According to the price characteristics, we determine the appropriate number of hidden layer nodes and build a BP neural network price prediction model based on the improved algorithm. Validation of examples shows that the error-corrected GDP forecast model is also better than the weighted GDP forecast model, which shows that error correction is also a better combination of forecasting methods. The forecast results of BP neural network have lower errors and monthly prices. The relative error of prediction is about 2.5%. Through comparison with the prediction results of the ARIMA model, in the daily price prediction, the relative error of the BP neural network prediction is 1.5%, which is lower than the relative error of the ARIMA model of 2%.

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References

2021A network traffic forecasting method based on SA optimized ARIMA–BP neural network 2019Forecasting Horticultural Products Price Using ARIMA Model and Neural Network Based on a Large-Scale Data Set Collected by Web Crawler 2020Stock price forecasting based on LLE-BP neural network model 2020A comparative study on the prediction of the BP artificial neural network model and the ARIMA model in the incidence of AIDS 2017Prediction of stock prices based on LM-BP neural network and the estimation of overfitting point by RDCI 2018Application and analysis of forecasting stock price index based on combination of ARIMA model and BP neural network 2019Exploration of stock index change prediction model based on the combination of principal component analysis and artificial neural network 2018Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model

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