基于最小二乘法的改进GM(1, 1)模型在上海市社会总抚养比预测中的应用

吴天魁,王波,顾基发

系统科学与数学 ›› 2015, Vol. 35 ›› Issue (1) : 75-84.

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系统科学与数学 ›› 2015, Vol. 35 ›› Issue (1) : 75-84. DOI: 10.12341/jssms12504
论文

基于最小二乘法的改进GM(1, 1)模型在上海市社会总抚养比预测中的应用

    吴天魁1,王波2,顾基发2
作者信息 +

FORECAST OF TOTAL SOCIAL DEPENDENCY RATIO OF SHANGHAI BASED ON THE IMPROVED GM(1, 1) MODEL OF LEAST SQUARE METHOD

    WU Tiankui1 , WANG Bo2 , GU Jifa2
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摘要

由于上海市社会总抚养比受到诸多因素的影响,导致数据波动性较大,如果采用普通灰色预测模型无法更加准确地进行预测,因此文章提出了一种改进的GM(1, 1)模型.通过将改进模型与普通模型进行对比得出采用二次多项式拟合的改进模型预测精度较高,因此文章采用二次多项式模拟的改进模型进行预测.将该改进模型应用于上海市社会总抚养比预测中,并结合2007--2011年上海市社会总抚养比数据建立新的预测模型,并用2012年数据对模型进行验证合格,可以用来预测未来几年上海市社会总抚养比,便于该市对未来经济的发展宏观调控.结果表明该预测方法是合理可行的,为其他相关预测提供了理论依据.

Abstract

The total dependency ratio of Shanghai is affected by many factors, leading to volatile data, it can not be predicted more accurately by gray prediction model, so the article proposed an improved GM(1, 1) model. Will improve the precision of the model were compared with the original model obtained using quadratic polynomial fitting improved model prediction precision higher than the original model, so quadratic polynomial fitting GM(1, 1) improved model is adopted. And use this improved model into the prediction of the total dependency ratio of Shanghai, and combined total dependency ratio data from 2007 to 2011 in Shanghai, a new forecasting model is built, and the model with the 2012 data to verify eligibility can be used to predict the total dependency ratio of Shanghai next few years to facilitate the economic development of the city of the future macro. The results show that the prediction method is reasonably practicable, and provides a theoretical basis for other related forecasts.

关键词

社会总抚养比 / 上海 / 预测 / 最小二乘法 / GM(1 / 1)模型.

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吴天魁 , 王波 , 顾基发. 基于最小二乘法的改进GM(1, 1)模型在上海市社会总抚养比预测中的应用. 系统科学与数学, 2015, 35(1): 75-84. https://doi.org/10.12341/jssms12504
WU Tiankui , WANG Bo , GU Jifa. FORECAST OF TOTAL SOCIAL DEPENDENCY RATIO OF SHANGHAI BASED ON THE IMPROVED GM(1, 1) MODEL OF LEAST SQUARE METHOD. Journal of Systems Science and Mathematical Sciences, 2015, 35(1): 75-84 https://doi.org/10.12341/jssms12504
中图分类号: 91B84   
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