Research on E-commerce Economic Forecasting Based on PSO Optimized Twin Support Vector Machine
In order to improve the performance of e-commerce economic forecasting and solve the problem of low fore-casting accuracy caused by complex time series,many features and complex user needs,Gemini support vector machine was used to forecast e-commerce economy.Firstly,the economic characters of e-commerce were obtained and then the economic prediction model of Twin Support Vector Machines(TWSVM)was established,and the parameters such as the canonical factor of TWSVM were extracted.Several groups of parameters were randomly initialized to build a particle swarm.Particle swarm optimization(PSO)algorithm was used to search the best parameters of TWSVM to generate the best TWSVM model suitable for e-commerce economic prediction.Through PSO optimization,the optimal TWSVM pa-rameters were obtained.Finally,the best TWSVM model was used to predict e-commerce.In the example simulation,the prediction accuracy of TWSVM algorithm optimized by PSO was higher than 90%,mainly based on the economic sales amount and growth rate of e-commerce.
E-commerce economyTwin support vector machineParticle swarm optimizationPerplanar
NETL
NSTL
万方数据
