计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 485-487.doi: 10.11896/JsJkx.190900168

• 数据库 & 大数据 & 数据科学 • 上一篇    下一篇

基于GM(1,1)-SVM组合模型的中长期人口预测研究

徐翔燕, 侯瑞环   

  1. 塔里木大学信息工程学院 新疆 阿拉尔 843300
  • 发布日期:2020-07-07
  • 通讯作者: 侯瑞环(1073293432@qq.com)
  • 作者简介:1036269601@qq.com
  • 基金资助:
    塔里木大学校长基金青年创新资金项目(TDZKQN201824)

Medium and Long-term Population Prediction Based on GM(1,1)-SVM Combination Model

XU Xiang-yan and HOU Rui-huan   

  1. College of Information Engineering,Tarim University,Alar,XinJiang 843300,China
  • Published:2020-07-07
  • About author:XU Xiang-yan, born in 1990, master, lecturer. Her main research interests include intelligent optimization algorithm and so on.
    HOU Rui-huan, born in 1986, master, lecturer.His main research interests include nonparametric statistics and so on.
  • Supported by:
    This work was supported by Tarim University President Fund Young Innovation Fund ProJect(TDZKQN201824).

摘要: 准确预测未来人口数量,对制定相关经济政策具有现实意义。文中针对人口中长期预测影响因素较复杂、可用历史数据较少、单一模型局限性等特点,构建了灰色预测和支持向量机的组合预测模型。该模型将灰色预测模型和支持向量机模型进行组合,利用标准差法确定模型的权值信息,将模型应用于一师阿拉尔市人口的中长期预测,选取一师阿拉尔市1997-2017年的人口数据进行分析,对2018-2022年的数据进行预测。结果表明:与单一模型相比较,组合模型预测精度更高,相对误差低,且预测结果比较稳定,结果更符合实际。

关键词: 灰色预测, 支持向量机, 组合模型

Abstract: Accurate prediction of future population is of practical significance for the formulation of relevant economic policies.In this paper,a combined prediction model of grey and support vector machine is contructed according to the characteristics of complicated influencing factors of medium and long-term prediction,less available historical data,and the limitations of single model.The model combines the grey prediction model with the support vector machine model and uses the standard deviation method to determine the weight information.The model is applied to the medium and long-term prediction of the population of Alar City,and the population data of the first division of Alar City from 1997 to 2017 is selected for analysis,to predict the data 2018 to 2022.The result shows that,compared with the single model,the combined model has higher prediction accuracy and lower relative error,and the prediction result is relatively stable and more realistic.

Key words: Combined model, Grey prediction, Support vector machine

中图分类号: 

  • C924
[1] 郭雪峰,黄健元,王欢.改进的灰色模型在流动人口预测中的应用.统计与决策,2018(8):76-79.
[2] 蒋若凡,姜玉梅,李菲雅.基于灰色PSO-BP人口预测模型的研究与应用.西北人口,2011,32(3):23-26.
[3] 龙会典,严广乐.基于改进的GM(1,1)—Markov链组合模型广东省单位GDP能耗预测.数理统计与管理,2017,36(2):200-207.
[4] 李凯,张涛.基于组合插值的GM(1,1)模型背景值的改进.计算机应用研究,2018,35(10):2994-2999.
[5] 吴文泽,张涛.GM(1,1)模型的改进及应用.统计与决策,2019(9):15-18.
[6] 徐丽丽,李洪,李劲.基于灰色预测和径向基网络的人口预测研究.计算机科学,2019,46(Z1):431-435.
[7] 解伟,潘文明,王成化,等.基于支持向量机的省级电网中长期投资规模预测模型研究.工业技术经济,2019(8):154-160.
[8] 贾娜,郭佳欣,花军,等.采用支持向量机算法对金刚石锯片锯切木材表面粗糙度的预测.东北林业大学学报,2019,47(10):85-89.
[9] 徐路路,王芳.基于支持向量机和改进粒子群算法的科学前沿预测模型研究.情报科学,2019,37(8):22-28.
[10] 宋晓华,祖丕娥,伊静,等.基于改进GM(1,1)和SVM的长期电量优化组合预测模型.中南大学学报(自然科学版),2012,43(5):1803-1807.
[11] BATES J M,GRANGER C W.Combination of Forecasts.Operational Res-Ouart,1969,20(4):451-468.
[12] LIU S L,HU Z Q,CHI X K.The research of power load forecasting method on combination forecasting model.Information Science and Engineering,2010(26).
[13] 吴静敏,左洪福,陈勇.基于免疫粒子群算法的组合预测方法.系统工程理论方法应用,2006,15(3):229-233.
[1] 侯夏晔, 陈海燕, 张兵, 袁立罡, 贾亦真.
一种基于支持向量机的主动度量学习算法
Active Metric Learning Based on Support Vector Machines
计算机科学, 2022, 49(6A): 113-118. https://doi.org/10.11896/jsjkx.210500034
[2] 单晓英, 任迎春.
基于改进麻雀搜索优化支持向量机的渔船捕捞方式识别
Fishing Type Identification of Marine Fishing Vessels Based on Support Vector Machine Optimized by Improved Sparrow Search Algorithm
计算机科学, 2022, 49(6A): 211-216. https://doi.org/10.11896/jsjkx.220300216
[3] 陈景年.
一种适于多分类问题的支持向量机加速方法
Acceleration of SVM for Multi-class Classification
计算机科学, 2022, 49(6A): 297-300. https://doi.org/10.11896/jsjkx.210400149
[4] 邢云冰, 龙广玉, 胡春雨, 忽丽莎.
基于SVM的类别增量人体活动识别方法
Human Activity Recognition Method Based on Class Increment SVM
计算机科学, 2022, 49(5): 78-83. https://doi.org/10.11896/jsjkx.210400024
[5] 武玉坤, 李伟, 倪敏雅, 许志骋.
单类支持向量机融合深度自编码器的异常检测模型
Anomaly Detection Model Based on One-class Support Vector Machine Fused Deep Auto-encoder
计算机科学, 2022, 49(3): 144-151. https://doi.org/10.11896/jsjkx.210100142
[6] 侯春萍, 赵春月, 王致芃.
基于自反馈最优子类挖掘的视频异常检测算法
Video Abnormal Event Detection Algorithm Based on Self-feedback Optimal Subclass Mining
计算机科学, 2021, 48(7): 199-205. https://doi.org/10.11896/jsjkx.200800146
[7] 陈慧琴, 郭贯成, 秦朝轩, 李兆碧.
基于GM-LSTM模型的南京市老年人口预测研究
Research on Elderly Population Prediction Based on GM-LSTM Model in Nanjing City
计算机科学, 2021, 48(6A): 231-234. https://doi.org/10.11896/jsjkx.200900142
[8] 郭福民, 张华, 胡瑢华, 宋岩.
一种基于表面肌电信号的腕部肌力估计方法研究
Study on Method for Estimating Wrist Muscle Force Based on Surface EMG Signals
计算机科学, 2021, 48(6A): 317-320. https://doi.org/10.11896/jsjkx.200600021
[9] 卓雅倩, 欧博.
噪声环境下的人脸防伪识别算法研究
Face Anti-spoofing Algorithm for Noisy Environment
计算机科学, 2021, 48(6A): 443-447. https://doi.org/10.11896/jsjkx.200900207
[10] 雷剑梅, 曾令秋, 牟洁, 陈立东, 王淙, 柴勇.
基于整车EMC标准测试和机器学习的反向诊断方法
Reverse Diagnostic Method Based on Vehicle EMC Standard Test and Machine Learning
计算机科学, 2021, 48(6): 190-195. https://doi.org/10.11896/jsjkx.200700204
[11] 王友卫, 朱晨, 朱建明, 李洋, 凤丽洲, 刘江淳.
基于用户兴趣词典和LSTM的个性化情感分类方法
User Interest Dictionary and LSTM Based Method for Personalized Emotion Classification
计算机科学, 2021, 48(11A): 251-257. https://doi.org/10.11896/jsjkx.201200202
[12] 曹素娥, 杨泽民.
基于聚类分析算法和优化支持向量机的无线网络流量预测
Prediction of Wireless Network Traffic Based on Clustering Analysis and Optimized Support Vector Machine
计算机科学, 2020, 47(8): 319-322. https://doi.org/10.11896/jsjkx.190800075
[13] 马创, 吕孝飞, 梁炎明.
基于GA-SVM的农产品质量分类
Agricultural Product Quality Classification Based on GA-SVM
计算机科学, 2020, 47(6A): 517-520. https://doi.org/10.11896/JsJkx.190900184
[14] 宋岩, 胡瑢华, 郭福民, 袁新亮, 熊睿洋.
基于sEMG的改进SVM+BP肌力预测分层算法
Improved SVM+BP Algorithm for Muscle Force Prediction Based on sEMG
计算机科学, 2020, 47(6A): 75-78. https://doi.org/10.11896/JsJkx.190900143
[15] 方梦琳, 唐文兵, 黄鸿云, 丁佐华.
基于模糊信息分解与控制规则的移动机器人沿墙导航
Wall-following Navigation of Mobile Robot Based on Fuzzy-based Information Decomposition and Control Rules
计算机科学, 2020, 47(6A): 79-83. https://doi.org/10.11896/JsJkx.191000158
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!