The Role of Statistical Regularization Methods in Preventing Overfitting in Predictive Modeling and Forecasting

Posted: Nov 29, 2021

• Maria Anderson
• Owen Allen
• Henry Nguyen

Abstract

The challenge of overfitting represents one of the most persistent obstacles in predictive modeling and forecasting applications across diverse domains. Statistical regularization methods have emerged as fundamental tools in the machine learning arsenal to address this challenge by imposing constraints on model complexity and parameter estimates. While the theoretical foundations of regularization techniques such as ridge regression, lasso, and elastic net are well-established in cross-sectional contexts, their application to time-series forecasting presents unique theoretical and practical considerations that remain under-explored in contemporary literature. This research addresses a critical gap in understanding how regularization methods can be optimally adapted for forecasting applications where temporal dependencies, structural breaks, and evolving data-generating processes complicate the bias-variance trade-off. Traditional regularization approaches often fail to account for the sequential nature of time-series data, leading to suboptimal performance in out-of-sample forecasting. Our work introduces a novel framework that integrates temporal regularization constraints with spatial smoothing techniques, creating a more robust approach to overfitting prevention in dynamic forecasting environments.