Vector Regression

Vector regression aims to predict a continuous multi-dimensional output variable based on input features, focusing on accurately modeling complex relationships and handling high-dimensional data. Current research emphasizes improving model accuracy and interpretability through techniques like ensemble methods (combining predictions from multiple models), regularized regression (e.g., Lasso, Ridge), and support vector regression, often coupled with methods for explaining model predictions. These advancements are impacting diverse fields, enabling more accurate nowcasting of economic indicators, improved causal inference from time-series data, and enhanced signal processing through techniques like anisotropic diffusion filtering.