[1]张宗华,张海全,李师航,等.基于加权滑动平均的磁盘使用率预测模型[J].深圳大学学报理工版,2016,33(1):72-79.[doi:10.3724/SP.J.1249.2016.01072]
 Zhang Zonghua,Zhang Haiquan,Li Shihang,et al.Disk usage prediction based on an improved weighted moving average method[J].Journal of Shenzhen University Science and Engineering,2016,33(1):72-79.[doi:10.3724/SP.J.1249.2016.01072]
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基于加权滑动平均的磁盘使用率预测模型()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第33卷
期数:
2016年第1期
页码:
72-79
栏目:
电子与信息科学
出版日期:
2016-01-20

文章信息/Info

Title:
Disk usage prediction based on an improved weighted moving average method
文章编号:
201601010
作者:
张宗华1张海全1 李师航2牛新征3
1)南京南瑞集团公司流程与信息管理中心,江苏南京 211106
2)西南财经大学经济信息工程学院,四川成都 611130
3)电子科技大学计算机科学与工程学院,四川成都 611731
Author(s):
Zhang Zonghua1 Zhang Haiquan1 Li Shihang2 and Niu Xinzheng3
1) Process and Information Management Center, NARI Group Corporation, Nanjing 211106, Jiangsu Province, P.R.China
2) College of Economics and Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130,Sichuan Province, P.R.China
3) College of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan Province, P.R.China
关键词:
计算技术加权滑动平均模型磁盘使用率自相关和偏自相关系数法拉依达准则权重转移多新息递推最小二乘法
Keywords:
This paper proposes an improved weighted moving average (WMA) model to predict the usage of disks in the near future. Considering the characteristics of showing gentle change in disk usage and the requirements with small lag we firstly utilize the autocorrelation and partial autocorrelation coefficient method to determine the order of the model. After processing the series the minimum lag can be calculated on the premise without affecting the accuracy. Additionally weights transferring combined with the Pauta criterion is used to balance the weight. At last we estimate the parameters by using the multiple innovation recursive least squares to improve the result of prediction. According to the simulation result by Matlab this algorithm is proved to have less result errors and a smaller lag. It provides a better prediction effect as compared with the original WMA model.
分类号:
TP 301.6
DOI:
10.3724/SP.J.1249.2016.01072
文献标志码:
A
摘要:
为能提前做好扩容准备,提出一种改进的加权滑动平均(weighted moving average, WMA)模型,用以预测未来短期内磁盘的使用率. 针对磁盘使用率序列变化较为平缓、要求滞后较小的特性,采用自相关和偏自相关系数法对模型定阶,处理数据后,在不影响精度的前提下计算最小滞后值,并使用结合了拉依达准则的权重转移法来均衡权重,用多新息递推最小二乘法对参数进行更精确的估计,以提高预测的准确性. 通过Matlab仿真实验可知,该算法预测误差小,滞后性弱,与原始WMA模型相比,具有更好的预测效果.
Abstract:
computing technology; weighted moving average model; disk usage; autocorrelation and partial autocorrelation coefficient method; Pauta criterion; transferring weight; multiple innovation recursive least squares

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备注/Memo

备注/Memo:
Received:2015-05-01;Accepted:2015-12-07
Foundation:National Natural Science Foundation of China (61300192);National Key Technology Support Program of China (2013BAH33F00);Research Foundation of NARI Group Corporation(SGTYHT/14-XX-194)
Corresponding author:Engineer Zhang Zonghua.E-mail: Zhangzonghua@sgepri.sgcc.com.cn
Citation:Zhang Zonghua,Zhang Haiquan,Li Shihang,et al.Disk usage prediction based on an improved weighted moving average method[J]. Journal of Shenzhen University Science and Engineering, 2016, 33(1): 72-79.(in Chinese)
基金项目:国家自然科学基金资助项目(61300192) ;国家科技支撑计划资助项目(2013BAH33F00);南京南瑞集团公司研究基金资助项目(SGTYHT/14-XX-194)
作者简介:张宗华(1977—),男,南京南瑞集团公司工程师.研究方向:电力信息化. E-mail:hangzonghua@sgepri.sgcc.com.cn
引文:张宗华,张海全, 李师航,等. 基于加权滑动平均的磁盘使用率预测模型[J]. 深圳大学学报理工版,2016,33(1):72-79.
更新日期/Last Update: 2016-01-14