A multidimensional indicator prediction assessment method based on time series and multiple linear regression model

This paper focuses on a multidimensional indicator prediction assessment method based on time series and multiple linear regression modelling. Through in-depth analysis of relevant data, principal component analysis is used to achieve dimension reduction of indicators. The process of constructing a multiple linear regression model is elaborated in detail, including key steps such as data standardization and parameter estimation. Combined with the time series model for prediction, the accuracy and reliability of the prediction are improved. The research results show that this method has significant advantages in the prediction and assessment of multidimensional indicators and provides strong support for decision-making in related fields.