MATLAB Implementation of Support Vector Machine for Stock Prediction and Simple Linear Regression

Support Vector Machine (SVM) is a widely-used machine learning algorithm applicable for tasks such as stock prediction and univariate linear regression. As a supervised learning method, SVM operates by identifying an optimal hyperplane to classify data or perform regression analysis. In MATLAB implementations, key functions like fitcsvm for classification and fitrsvm for regression are typically employed, utilizing kernel functions (linear, polynomial, or RBF) to handle non-linear relationships. For stock prediction applications, SVM analyzes historical market data and relevant factors to forecast future stock trends. The algorithm processes features such as price movements, trading volumes, and technical indicators through careful feature engineering and parameter optimization using functions like fitrsvm with appropriate kernel selections. Univariate linear regression represents a straightforward yet effective regression analysis method that establishes a linear model to describe the relationship between two variables. In MATLAB, this can be implemented using the fitlm function or simple matrix operations (X\y) to perform least squares fitting. Through fitting the univariate linear regression model, we can predict the value of one variable given another variable's value. Therefore, employing methods like Support Vector Machines and univariate linear regression with proper MATLAB coding techniques - including data preprocessing, model training, and cross-validation - enables more accurate stock predictions and regression analysis outcomes. Implementation typically involves separating training and testing datasets, normalization procedures, and performance evaluation using metrics like RMSE or classification accuracy.