Comparison of Machine Learning Models and Traditional Models for Forecasting in the Economy

Abstract

With the rapid development of information technology, machine learning models have gradually become a new favorite in the field of economic forecasting. However, traditional models such as linear regression and time series analysis are still widely used in practice. This paper provides an in-depth comparison between machine learning models and traditional models in terms of performance, interpretability and uncertainty modeling in economic forecasting. First, the advantages of machine learning models in handling large-scale, high-dimensional, and nonlinear data are explored from the perspective of performance. As well as the performance of traditional models in scenarios with small samples and high explanatory requirements. Secondly, attention is paid to the interpretability. The characteristics of traditional models that are easier to interpret due to their simple mathematical expressions are analyzed. As well as the challenges of machine learning models in terms of interpretability with the latest interpretable techniques. Finally, uncertainty modeling is studied in depth. The advantages of traditional models using probabilistic statistical methods are compared with the novel techniques introduced by machine learning models to provide more flexible uncertainty estimation in the economic domain. In practical applications, the basis for choosing machine learning models or traditional models in different scenarios is explored. The possibility of integrating the two is also pointed out with a view to providing a more comprehensive and flexible solution for future economic forecasting. This comprehensive comparison provides researchers and policy makers with a reference basis for choosing appropriate models in economic forecasting and some suggestions for future research directions.