Research on multidimensional evaluation and prediction modelling

Abstract

This research paper presents a comprehensive methodology for multidimensional evaluation and prediction modeling, integrating advanced statistical and computational techniques. It focuses on the development of a robust model that employs least squares interpolation, normalization, entropy weighting, and the coefficient of variation method for accurate data analysis and weight calculation. By analyzing data from 20 countries across 11 indicators, the study leverages TOPSIS, Random Forest Regression, and Grey Prediction Models to offer a nuanced understanding of complex systems. The findings demonstrate the model's effectiveness in providing reliable assessments and forecasts, underscoring its potential for widespread application in various fields.