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Study on the Prediction and Analysis of the Number of Enrollment

  • Xue Liu
  • Xiao-qiang Xi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)

Abstract

In the era of big data, the value of data has received unprecedented attention. Predictive analysis is an important direction of data application. Different data are suitable for different prediction models, and the prediction accuracy is different. In this paper, in order to accurately find a variety of data to adapt to the prediction model, the data of three different areas of high school enrollment in Shaanxi, Xi’an and China were predicted and analyzed. The results of polynomial fitting, grey model and grey prediction model based on wavelet transform are compared. After demonstration and analysis, the polynomial fitting is more effective to fill missing data. Grey prediction model and grey combination model based on wavelet transform are suitable for data prediction, and the grey combination model based on wavelet transform is more accurate than grey prediction model, and it is more suitable for the predictive analysis of enrollment number. The predicted results can provide reference and basis for the educational administrative departments to make decisions.

Keywords

Predictive analysis Polynomial fitting Grey model Wavelet transform 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Communications and Information EngineeringXi’an University of Posts and TelecommunicationsXi’anChina
  2. 2.Institute of Internet of Things and IT-Based IndustrializationUniversity of Posts and TelecommunicationsXi’anChina

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