Regression Prediction Using BP Neural Networks
Implementation of Regression Prediction Models with Backpropagation Neural Networks
SVM-Based Regression Prediction Analysis: Predicting Shanghai Composite Index Opening Prices
Support Vector Machines (SVM) can be applied to both classification and regression prediction tasks. This case study demonstrates SVM implementation for regression analysis to predict stock market indices. Effective prediction of major indices provides crucial insights for observing overall market trends, making Shanghai Composite Index forecasting particularly valuable. Using daily opening prices from 1990.12.20 to 2009.08.19, the SVM regression model achieved impressive results: Mean Squared Error (MSE) = 1.95029e-005 and R-squared coefficient R = 99.9345%, indicating highly accurate fitting. Key implementation involves using SVM regression algorithms (like SVR) with appropriate kernel functions and parameter optimization.
Regression Prediction Analysis Using SVM Neural Networks
Regression Prediction Analysis with SVM Neural Networks - Shanghai Stock Exchange Opening Index Forecasting
Neural Network Support Vector Machine (SVM)
Parameter optimization, classification prediction, and regression prediction with information granulation, implemented through machine learning algorithms.