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

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, forecast...

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Autor principal:	Shaobo Lu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/49f33a0a404e481f822a2630532a0be9
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spelling	oai:doaj.org-article:49f33a0a404e481f822a2630532a0be92021-11-22T01:11:19ZResearch on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model1687-527310.1155/2021/1026978https://doaj.org/article/49f33a0a404e481f822a2630532a0be92021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/1026978https://doaj.org/toc/1687-5273Based 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%.Shaobo LuHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Shaobo Lu Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model
description	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%.
format	article
author	Shaobo Lu
author_facet	Shaobo Lu
author_sort	Shaobo Lu
title	Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model
title_short	Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model
title_full	Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model
title_fullStr	Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model
title_full_unstemmed	Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model
title_sort	research on gdp forecast analysis combining bp neural network and arima model
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/49f33a0a404e481f822a2630532a0be9
work_keys_str_mv	AT shaobolu researchongdpforecastanalysiscombiningbpneuralnetworkandarimamodel
_version_	1718418279218806784

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

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