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

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shaobo Lu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Error Correction ◽  
Relative Error ◽  
Bp Neural Network ◽  
Arima Model ◽  
Forecast Model ◽  
Price Prediction ◽  
The Neural Network

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%.

scholarly journals Forecast of Waterway Cargo Turnover Volume Based on Genetic Algorithm to Optimize Neural Network Parameters

2021 ◽  
Vol 2083 (3) ◽  
pp. 032010
Author(s):  
Rong Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Network Parameters ◽  
The Neural Network ◽  
Target Effect ◽  
The Empirical Analysis ◽  
Better Than

Abstract The traditional BP neural network is difficult to achieve the target effect in the prediction of waterway cargo turnover. In order to improve the accuracy of waterway cargo turnover forecast, a waterway cargo turnover forecast model was created based on genetic algorithm to optimize neural network parameters. The genetic algorithm overcomes the trap that the general iterative method easily falls into, that is, the “endless loop” phenomenon that occurs when the local minimum is small, and the calculation time is small, and the robustness is high. Using genetic algorithm optimized BP neural network to predict waterway cargo turnover, and the empirical analysis of the waterway cargo turnover forecast is carried out. The results obtained show that the neural network waterway optimized by genetic algorithm has a higher accuracy than the traditional BP neural network for predicting waterway cargo turnover, and the optimization model can long-term analysis of the characteristics of waterway cargo turnover changes shows that the prediction effect is far better than traditional neural networks.

Long-Term Power Load Forecast Based on Improved BP Neural Network

2010 ◽  
Vol 439-440 ◽  
pp. 848-853
Author(s):  
Shuang Chen Li ◽  
Di Yuan
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Setup Time ◽  
Time Sequence ◽  
Bp Algorithm ◽  
Load Forecast ◽  
The Neural Network ◽  
Power Load

This article proposed the improvement BP algorithm which solved the neural network to restrain slow and easy well to fall into the partial minimum the question, through setup time sequence forecast model made the long-term power forecast, and made a comparison with the traditional BP natural network.

Pressure Prediction Technology of the Deep Strata Based on BP Neural Network

2010 ◽  
Vol 143-144 ◽  
pp. 28-31 ◽  
Author(s):  
Wei Li ◽  
Tie Yan ◽  
Ying Jie Liang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Gamma Ray ◽  
Forecast Model ◽  
Oil Field ◽  
The Neural Network ◽  
Natural Gamma ◽  
Gamma Ray Log ◽  
Deep Strata ◽  
Very High

. The accurate prediction of strata pressure is the base for safely, quality and efficiently drilling, decreasing hole problems and reasonable development of the reservoir. Because of the high cost, long cycle of the formation pressure measured method, which may influence the safety of drilling operation, thus a new method for predicting strata pressure, based on the BP neural network, is presented in this paper, and establishing process of the neural network forecast model are discussed in detail. This method takes the acoustic time, natural potential, natural gamma ray log data and pipe pressure test data as study sample, which has a very high accuracy. The paper predicts strata pressure of the Saertu oil field and Xingshugang oil field in Daqing, and the results show that relative error between the predicted data and experimental data is less than ±8.9%.

City Fire Forecasts and Analysis Based on Nonlinear Auto-Regressive Time-Series Model

2012 ◽  
Vol 241-244 ◽  
pp. 1550-1555 ◽  
Author(s):  
Sheng Peng Liu ◽  
Ye Zhang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Support Vector ◽  
Future Developments ◽  
Proposed Model ◽  
Network Method ◽  
Auto Regressive ◽  
The City

The forecasting to future developments of the city fire time series is a challenging task that has been addressed by many researchers due to the importance. In this paper, a Nonlinear Auto-Regressive (NAR) prediction model is applied to forecast the city fire data based on support vector regression. The performances of the NAR prediction model in city fire forecasting are compared with the BP neural network method. The experimental results show that the proposed model performs best.

scholarly journals Forecast of Football League Sales Effect Based on BP Neural Network

2020 ◽  
Author(s):  
Zhubo Xu ◽  
Weifeng Qin
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Percentage Difference ◽  
The Neural Network ◽  
Sample Data ◽  
Real Value ◽  
Ancient Times ◽  
The Difference

Abstract Football is one of the sports that is loved by people all over the world. Its sales ability in the league should not be underestimated. Moreover, football has been developed in our country since ancient times and has a huge fan base, and fans are the main target of football league sales. Predicting the sales effect of the football league is helpful for the seller to formulate a suitable sales strategy and avoid the problem of product surplus. This article mainly introduces the prediction research of football league sales effect based on BP neural network, and intends to provide ideas and methods for predicting the sales effect of football league. This paper puts forward the basic method of the sales effect prediction of the football league and the BP neural network football league sales effect prediction method to analyze and predict the sales effect of the football league. In addition, the steps of establishing BP neural network design, building BP neural network football league sales effect prediction model and applying BP neural network football league sales effect prediction model are also proposed. The experimental results of this article show that the difference between the fitting part of the neural network model and the real value of the football league sales effect sample data is in the range of , the error percentage difference is small, and the prediction results are valid。

Application of BP Neural Network in the Reliability Prediction

2011 ◽  
Vol 121-126 ◽  
pp. 3814-3818 ◽  
Author(s):  
Wei Jiang ◽  
Meng Zhang ◽  
Zhi Ling Chen ◽  
Yun Liu ◽  
Ning Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Bp Algorithm ◽  
Reliability Prediction ◽  
Reliability Parameters ◽  
The Neural Network ◽  
Training Samples ◽  
Neural Network Toolbox

Using neural network BP algorithm and the neural network toolbox of MATLAB, this paper presented a new reliability prediction model of the products. Its processes included that confirming training samples, putting up the network that was initialized, training the network and predicting reliability parameters of the products. At last reliability parameters of an example were predicted with the reliability prediction, the prediction effect was more perfect.

Research on underwear pressure prediction based on improved GA-BP algorithm

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pengpeng Cheng ◽  
Daoling Chen ◽  
Jianping Wang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Subjective Evaluation ◽  
Static State ◽  
Content Type ◽  
The Neural Network ◽  
Ga Algorithm ◽  
Neural Network Prediction ◽  
Network Prediction

PurposeFor comfort evaluation of underwear pressure, this paper proposes an improved GA algorithm to optimize the weight and threshold of BP neural network, namely PSO-GA-BP neural network prediction model.Design/methodology/approachThe objective parameters of underwear, body shape data, skin deformation and other data are selected for simulation experiments to predict the objective pressure and subjective evaluation in dynamic and static state. Compared with the prediction results of BP neural network prediction model, GA-BP neural network prediction model and PSO-BP neural network prediction model, the performance of each prediction model is verified.FindingsThe results show that the BP neural network model optimized by PSO-GA algorithm can accelerate the convergence speed of the neural network and improve the prediction accuracy of underwear pressure.Originality/valuePSO-GA-BP model provides data support for underwear design, production and processing and has guiding significance for consumers to choose underwear.

Time Series Forecast of Foundation Pit Deformation Based on BP Neural Network

2014 ◽  
Vol 556-562 ◽  
pp. 5979-5983 ◽  
Author(s):  
Jing Cao ◽  
Wen Yun Ding ◽  
Dang Shu Zhao ◽  
Hai Ming Liu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Measured Data ◽  
Engineering Optimization ◽  
Forecast Method ◽  
Foundation Pit ◽  
Time Series Forecast ◽  
Excavation Process

Combined with the advantage of BP neural network, a time series forecast method of foundation pit deformation based on BP neural network is proposed. According to the excavation process of foundation pit, the deformation forecast model is built by analyzing the measured data of early working stage. Then, the model is used to forecast the deformation of later working stage. Through an engineering optimization example, it is showed that this method is not only efficient, but also with good economic and practical value.

Prediction and Analysis of Forged Workpiece’s Precision in Hydraulic Press Based on BP Neural Network and Genetic Algorithm

2010 ◽  
Vol 97-101 ◽  
pp. 2598-2602 ◽  
Author(s):  
Yan Cong Li ◽  
Lian Hong Zhang ◽  
Chun Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Orthogonal Experiment ◽  
Hydraulic Press ◽  
Important Indicator ◽  
Network Training ◽  
The Neural Network ◽  
Forecast Result

Workpiece’s precision is an important indicator of hydraulic press. In order to accurately predict the accuracy of the part, a method that combined the genetic algorithm and neural network is put out. Design of orthogonal experiment (DOE) is used to determine the input samples of neural network training and testing samples. The output samples are obtained by finite element analysed method (FEA). Through optimizing weights and thresholds of BP neural network using genetic algorithms, prediction model of workpiece’s precision is established. The established predict model overcomes the shortcomings of slow to convergence and easy to fall into the local minimum point of BP neural network model . By comparing the neural network forecast result with FEA ‘s results, it can be seen that the established prediction model has good fitting and generalization ability. So the model can be used to predict the workpiece’s precision.

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