Analysis And Prediction Of The Government Statistical Data Under The Background Of Big Data

In recent years, a large-scale data growth phenomenon appears in the network economics, environmental science, internet technology and many other areas. The community officially enteres the era of big data.Big data as a kind of information capital and data resources will have a great impact on national governance, government decision-making and so on. This also makes many of traditional data processing and analysis algorithms can not meet the rapid growth of data need. This paper analyzes the government statistical methods in the background of big data, and the main work arrangement is as follows.The first chapter discusses the research background, significance and literature review at home and abroad, and puts forward the research question of this paper.In the second chapter, Bootstrap algorithm and Bag of Little Bootstrap algorithm are introduced. Bag of Little Bootstrap algorithm is the improved algorithm of Bootstrap. The idea and calculation process of the algorithm are given. It points out that BLB algorithm has high feasibility in the case of large amount of data.In the third chapter, the traditional accounting method is used to update the CPI accounting method from the aspects of accounting process and weight, and the method of sampling based on Bootstrap is put forward as expanding the sample size of the data, reducing the collection rate of the price collection point, while saving data acquisition costs, but also improving the prediction accuracy. With reference to the method of statistical network price consumption index, the weights in CPI accounting are improved, and the frequency of weight renewal is improved.The fourth chapter constructs the regression prediction model based on Bootstrap and BLB sampling method, and gives the corresponding algorithm. The given model is a good example of the advantages of Bootstrap and BLB sampling methods in statistical data processing and inference. In particular, the regression prediction method based on the BLB sampling method can realize the parallel operation in the case of large amount of data, which makes the model better suitable for large data regression analysis.In the fifth chapter, the regression model of the fourth chapter is used to test the regression model. The experimental results show that the Bootstrap regression algorithm has higher prediction accuracy than the traditional multiple linear regression model. The BLB regression model is applied to the prediction of CPI, which further validates that the BLB regression model has higher accuracy than the Bootstrap regression model.The sixth chapter summarizes the main contents of the paper and puts forward the further research on CPI accounting and regression prediction.