Regression Prediction Analysis Using SVM Neural Networks

Support Vector Machines (SVM) and Neural Networks (NN) are widely applied in regression prediction analysis. This article focuses on their application in predicting the Shanghai Stock Exchange opening index. Support Vector Machine is a supervised learning algorithm that performs regression analysis by finding an optimal hyperplane to maximize the margin between different classes while minimizing prediction errors. The implementation typically involves selecting appropriate kernel functions (such as linear, polynomial, or RBF kernels) and optimizing parameters through techniques like cross-validation. Neural Networks, inspired by biological nervous systems, process information and perform prediction analysis through interconnected nodes organized in layers. A typical implementation includes designing the network architecture (input layer, hidden layers, output layer), selecting activation functions (ReLU, sigmoid, tanh), and applying backpropagation algorithms for weight optimization. The training process often involves gradient descent optimization and regularization techniques to prevent overfitting. We will examine the advantages and limitations of both methods and compare their performance in Shanghai Stock Exchange opening index prediction. Through in-depth analysis of these algorithms' principles and practical applications, we can better understand their roles in financial market analysis and provide more accurate prediction results for investment decision-making. Key implementation considerations include feature engineering, data normalization, model validation methods, and performance metrics evaluation (such as MSE, RMSE, and R-squared scores).