GDP Prediction Using BP Neural Network Algorithm

Application of Combined BP Neural Network and Gray Prediction Model in GDP Forecasting

Accurate prediction of Gross Domestic Product (GDP) holds significant importance for national economic policy formulation and macro-control. This article explains how to integrate Backpropagation (BP) Neural Networks with the Gray Prediction Model GM(1,1) to construct a more reliable GDP forecasting model.

Advantages of BP Neural Networks in GDP Prediction: BP neural networks simulate the working mechanism of human brain neurons, enabling automatic learning of nonlinear relationships within GDP data. Their multilayer architecture can capture complex factors in economic development, including interactions among various economic indicators. During network training, weights are continuously adjusted through error backpropagation, ultimately establishing a mapping relationship between input economic indicators and output GDP values. Implementation typically involves defining network architecture (hidden layers/neurons), training with historical data using algorithms like gradient descent, and validating prediction accuracy.

Characteristics of Gray Prediction Model GM(1,1): The gray prediction model is particularly suitable for economic data with "small samples and poor information." It processes raw data through cumulative generation operations, weakening randomness while strengthening regularity to establish differential equation models. GM(1,1) demonstrates excellent performance in short-term economic forecasting, especially in GDP prediction scenarios with limited data volume. Code implementation involves data preprocessing through accumulation generation, parameter estimation for the whitening equation, and inverse accumulation to obtain predictions.

Construction Methodology for Combined Model: 1. Independently predict GDP using both BP neural network and GM(1,1) models 2. Analyze historical prediction error characteristics of both methods 3. Determine combination weights using optimal weighting methods (e.g., minimum variance weighting) 4. Establish linear combination forecasting equations

Value of Combined Model: This hybrid approach integrates BP neural network's nonlinear fitting capability with gray prediction's adaptability to small-sample data. Practical applications demonstrate that combined models generally achieve higher prediction accuracy and stability compared to single models, better handling uncertainties within economic systems. The implementation can be automated through Python/MATLAB scripts that calculate optimal weights based on historical error minimization.