Evaluating the Performance of Hybrid Forecasting Models Combining Machine Learning and Traditional Methods

Abstract

Hybrid forecasting models, which blend statistical and machine learning techniques, have gained significant traction to improve prediction accuracy across various fields. These models leverage the strengths of both traditional statistical methods and modern machine learning algorithms, creating a synergistic approach that can better capture complex patterns in data. This study offers a comprehensive evaluation framework designed to assess the effectiveness of hybrid forecasting models. Key performance metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), R-squared, and information criteria are used to evaluate model accuracy and reliability rigorously. The research involves meticulous data preparation, model development, and validation processes, ensuring that each hybrid model is thoroughly tested and optimized. This study aims to identify the most effective combinations of statistical and machine learning components by systematically analyzing various hybrid models performance and error patterns. The findings contribute to advancing forecasting methodologies, offering valuable insights that can be applied across diverse domains. Ultimately, this research enhances the ability to make accurate and reliable predictions, which is crucial for decision-making in finance, economics, healthcare, and environmental science. It can be inferred easily that hybrid forecasting models have a specific and popular future.