Tax forecasting based on Adaboost algorithm and BP neural network

Abstract

In view of the lower accuracy of traditional tax forecasting models,the authors put forward a method of combining the Adaboost algorithm with BP neural network to forecast revenue.Firstly,the method performed the pretreatment for the historical tax data and initialized the distribution weights of test data;secondly,it initialized the weights and thresholds of BP neural network,and used BP neural network as a weak predictor to train the tax data repeatedly and adjust the weights;finally,it made more weak predictors of BP neural network to form new strong predictors by Adaboost algorithm and forecasted.The authors also carried out simulation experiment for the tax data of China from 1990 to 2010.The results show that this method has reduced the relative value of mean error from 0.50% to 0.18% compared to the traditional BP network,has effectively reduced the effect when single BP gets trapped in local minima,and has improved the prediction accuracy of network.