Prediction of Economic Operation Index Based on Support Vector Machine

Abstract

Economic forecasting is not only an important field of economic research but also attracts extensive public attention. The results of the study are directly related to the accurate understanding and view of the economic situation, which in turn affects the rational formulation of macroeconomic policies. However, traditional estimation methods are often limited by expert experience and simple mathematical models, which are difficult to deal with on nonlinear models and do not meet the objective requirements for predicting macroeconomic performance indicators. Support vector machine is a popular new data mining technique. Benefiting from its good theoretical foundation and good generalization performance, SVM has become the starting point of research in recent years. Combining support vector machine research, fuzzy theory, and macroeconomic performance estimation, it attempts to develop a method for predicting macroeconomic function indicators based on support vector machines and expand the theory and projects of support vector machines. In addition, empirical evaluations of early financial forecasts are conducted to integrate theoretical and practical data. The characteristics of the traditional evaluation system are discussed in detail, and the standard model of the prediction system is established, including classical and modern forecasting theories and their forecasting systems. The theory of statistical learning and the theory and basic features of support vector machines are also discussed. For the analysis of the internal relationship between the distribution pattern, SVM, and the forecast of macroeconomic performance indicators, predictable economic activity forecast can be considered as a distribution system. Combining SVM with economic forecasting, SWE intelligent economic operation index forecasting is used for the first time, the automatic selection of symbolic parameters is recognized, and some algorithms and in-depth analysis methods are provided. The experimental results show that the prediction of economic operation indicators based on support vector machine technology is 57% faster than the traditional prediction method and the prediction accuracy rate is as high as 98.56%.