Open AccessA prediction model for population change using ARIMA model based on feature extraction
Author(s) -
Wei Li,
Zhongyu Su,
Pengcheng Guo
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1324/1/012083
Subject(s) - autoregressive integrated moving average , principal component analysis , population , dimension (graph theory) , variety (cybernetics) , computer science , econometrics , component (thermodynamics) , artificial intelligence , time series , statistics , mathematics , machine learning , demography , sociology , physics , pure mathematics , thermodynamics
Population is a social entity with complex contents and a variety of social relations. It includes gender, age and natural composition, as well as a variety of social composition, social relations, economic composition and economic relations. It is difficult to analyze the population change in large number and high dimension over the years in China. A prediction model for population change using ARIMA model based on feature extraction is proposed. It provides the reduction of population change indicators and predicts the population change in the future. Principal component analysis is firstly used to transform this problem in high-dimensional space into low-dimensional space, making the problem simple and intuitive. These less comprehensive indexes obtained are not related to each other and provide most information of the original indexes. The data of the national population change from 1979 to 2015 is effectively reduced to one principal component. On the basis of the analysis results, a prediction model using ARIMA model is established to predict the population change in the next few years.