Dynamic Evaluation and Prediction for Macroeconomic Based on IGA-LSSVM

Abstract

Among all statistics, government statistics plays an extremely important role in socioeconomic activities, while the continuous changes of external factors bring new challenges to government statistics and have a great impact on the quality of statistics. With the rapid development of the economy and the maturity of the market economy system, the demand for government statistics from all walks of life has become more and more vigorous. In order to effectively improve the accuracy of macroeconomic outflow forecasting and further secure economic production, a weighted least squares support vector machine (LS-SVM) optimized with the immune genetic algorithm (IGA) is proposed to build a macroeconomic outflow forecasting model. For the nonlinearity, time-varying, and complexity of the economic outflow system, a new weighted strategy function is proposed to improve the LS-SVM, and then the IGA is introduced to optimize the improved LS-SVM with the kernel parameter δ and the regularization parameter γ. Finally, an experimental analysis is conducted using economic emergence data from economic history. The results show that the maximum relative error of forecasting using the model is 2.763%, the minimum relative error is 0.705%, the average relative error is 1.3298%, and the model has faster convergence, stronger generalization ability, and higher forecasting accuracy than other forecasting models.