[1]王电钢,黄林,常健,等.基于ARIMA和CART的负载预测模型[J].深圳大学学报理工版,2019,36(No.3(221-346)):245-251.[doi:10.3724/SP.J.1249.2019.03229]
 WANG Diangang,HUANG Lin,CHANG Jian,et al.Load forecasting model based on ARIMA and CART[J].Journal of Shenzhen University Science and Engineering,2019,36(No.3(221-346)):245-251.[doi:10.3724/SP.J.1249.2019.03229]
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基于ARIMA和CART的负载预测模型()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第36卷
期数:
2019年No.3(221-346)
页码:
245-251
栏目:
【电子与信息科学】
出版日期:
2019-05-20

文章信息/Info

Title:
Load forecasting model based on ARIMA and CART
文章编号:
201903004
作者:
王电钢1黄林1常健1梅克进2牛新征2
1) 国网四川省电力公司信息通信公司,四川成都 610015
2) 电子科技大学计算机科学与工程学院,四川成都 611731
Author(s):
WANG Diangang1 HUANG Lin1 CHANG Jian1 MEI Kejin2 and NIU Xinzheng2
1) State Grid Sichuan Electric Power Company Information and Communication Corporation, Chengdu 610015, Sichuan Province, P.R.China
2) School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan Province, P.R.China
关键词:
计算机应用技术 时间序列 负载预测 最小二乘法 自回归差分滑动平均模型 分类回归树
Keywords:
computer application technology time series load forecasting least square method auto regressive integrated moving average (ARIMA) classification and regression tree (CART)
分类号:
TP301.6
DOI:
10.3724/SP.J.1249.2019.03229
文献标志码:
A
摘要:
主机资源的负载预测对其运营维护工作具有重要意义.传统负载预测方法通常采用线性时间序列模型拟合负载数据,而负载受复杂的内外部环境影响,线性模型无法很好地表征负载数据规律.为提高模型的精度,提出将负载信息分解为线性部分和非线性部分的思想,并将自回归差分滑动平均(autoregressive integrated moving average,ARIMA)模型和分类回归树(classification and regression tree,CART)模型相结合进行预测.通过加权最小二乘法改进的ARIMA预测线性部分,通过边界判定优化的CART预测非线性部分,并结合两者获得综合预测结果.在真实负载数据集下进行对比实验,结果表明,改进后的算法预测精度相比传统方法提高了15%以上,且对偏远值和不同的时间间隔都均有良好的适应性.
Abstract:
The load forecasting of host resources is of great significance to the operation and maintenance work. The traditional load forecasting methods usually use linear model to fit the load data. In the actual operation of equipment, the load is affected by the complex internal and external environment where many nonlinear factors are included. The linear time series model can not well characterize the law of load data. In order to improve the model accuracy, the idea of decomposing the load information into linear part and nonlinear parts is proposed, and the autoregressive integrated moving average (ARIMA) model and the classification and regression tree (CART) model are combined for prediction. Specifically, the ARIMA model improved by the weighted least squares method is used to predict the linear part and the CART optimized by the boundary determination is used to predict the nonlinear part, and the prediction results of the two parts are combined to obtain a comprehensive prediction result. The comparison experiments are carried out on the real load dataset. The results show that the prediction accuracy of the proposed algorithm is improved by more than 15% compared with the traditional method, and it has good adaptability to remote values and different time intervals.

参考文献/References:

[1] 李刚, 王文婧. 基于时间序列的存储负载预警研究[J].智能计算机与应用, 2018, 8(3): 188-190,194.
LI Gang, WANG Wenjing. Storage load forecasting research based on time series[J]. Intelligent Computer & Applications, 2018, 8(3): 188-190,194.(in Chinese)
[2] 杨建萍. 基于ARIMA模型的用电量时间序列建模和预报[J]. 工程数学学报, 2008(4): 611-615.
YANG Jianping. ARIMA time series modeling and forecasting of electricity consumption[J]. Chinese Journal of Engineering Mathematics, 2008, 25(4): 611-615.(in Chinese)
[3] 王永斌, 柴峰, 李向文, 等. ARIMA模型与残差自回归模型在手足口病发病预测中的应用[J]. 中华疾病控制杂志, 2016, 20(3): 303-306.
WANG Yongbin, CHAI Feng, LI Xiangwen, et al. Application of ARIMA model and auto-regressive model in prediction on incidence of hand-foot-mouth disease[J]. Chinese Journal of Disease Control & Prevention, 2016, 20(3): 303-306.(in Chinese)
[4] 单锐, 王国芳, 黄威, 等. 基于改进谱共轭梯度思想的ARIMA模型参数估计优化法[J]. 兰州理工大学学报, 2018, 44(4): 152-156.
SHAN Rui, WANG Guofang, HUANG Wei, et al. Optimization method for ARIMA model parameter estimation based on idea of improved spectral conjugate[J]. Journal of Lanzhou University of Technology, 2018, 44(4): 152-156.(in Chinese)
[5] 单锐, 刘雅宁, 刘文, 等. 改进的差分自回归移动平均模型的共轭梯度参数估计法[J]. 河南科技大学学报自然科学版, 2015, 36(4): 85-90,9.
SHAN Rui, LIU Yaning, LIU Wen, et al. Improved conjugate gradient parameter estimation for autoregressive integrated moving average model[J]. Journal of Henan University of Science & Technology, 2015, 36(4): 85-90,9.(in Chinese)
[6] 张宗华, 张海全, 魏驰, 等. 基于加权改进的AR模型的负载预测研究[J]. 计算机测量与控制, 2016, 24(3): 248-251.
ZHANG Zonghua, ZHANG Haiquan, WEI Chi, et al. Load prediction based on an improved AR model with weighting[J]. Computer Measurement & Control, 2016, 24(3): 248-251.(in Chinese)
[7] 易仁杰, 余剑, 吴标, 等. 基于加权双曲线定位的DV-hop改进算法[J]. 火力与指挥控制, 2016, 41(12): 96-100.
YI Renjie, YU Jian, WU Biao, et al. An improved DV-hop algorithm based on weighted hyperbolic positioning[J]. Fire Control and Command Control, 2016, 41(12): 96-100.(in Chinese)
[8] 严彦文. 基于ARIMA模型的山东省GDP的分析与预测[J]. 数学理论与实践, 2018, 48(4): 285-292.
YAN Yanwen. Analysis and forecast of GDP in Shandong Province based on ARIMA model[J]. Mathematics in Practice & Theory, 2018, 48(4): 285-292.(in Chinese)
[9] 张亮, 宁芊. CART决策树的两种改进及应用[J]. 计算机工程与设计, 2015, 36(5): 1209-1213.
ZHANG Liang, NING Qian. Two improvements on CART decision tree and its application[J]. Computer Enginee-ring & Design, 2015, 36(5): 1209-1213.(in Chinese)
[10] 王伟, 薛丰昌, 史达伟, 等. 基于CART算法的夏季干旱预测模型研究及应用[J]. 气象科学, 2016, 36(5): 661-666.
WANG Wei, XUE Fengchang, SHI Dawei, et al. Research on summer drought prediction model based on CART algorithm[J]. Journal of the Meteorological Sciences, 2016, 36(5): 661-666.(in Chinese)
[11] 刘江岩, 陈焕新, 王江宇, 等. 基于数据挖掘算法的地铁站内温度时序预测方法[J]. 工程热物理学报, 2018,39(6): 1316-1321.
LIU Jiangyan, CHEN Huanxin, WANG Jiangyu, et al. Time series prediction of the infoor temperature in the subway station based on data mining techniques[J]. Journal of Engineering Thermophysics, 2018, 39(6): 1316-1321.(in Chinese)
[12] 李鑫鑫, 桑燕芳, 谢平, 等. 基于离散小波分解的水文随机过程平稳性检验方法[J]. 系统工程理论与实践, 2018, 38(7): 1897-1904.
LI Xinxin, SANG Yanfang, XIE Ping, et al. A method for testing the stationarity of stochastic hydrological process based on discrete wavelet transform[J]. Systems Engineering-Theory and Practice, 2018, 38(7): 1897-1904.(in Chinese)
[13] 魏峰远, 郭继发, 李卫贤. 基于AIC准则的回归方法在建筑物变形分析中的应用[J]. 工程勘察, 2007(7): 46-48.
WEI Fengyuan, GUO Jifa, LI Weixian. Application of regression method based on AIC norm in building deformation analysis[J]. Geotechnical Investigation & Surveying, 2007, 13(7): 46-48.(in Chinese)
[14] 周美琴. 单位代价收益敏感决策树分类算法及其剪枝算法的研究[D]. 南宁:广西师范大学, 2016.
ZHOU Meiqin. Research on unit cost gains sensitive decision tree classification and pruning algorithm[D]. Nanning: Guangxi Normal University, 2016.(in Chinese)
[15] 范洁, 杨岳湘. 决策树后剪枝算法的研究[J].湖南广播电视大学学报, 2005(1): 54-56.
FAN Jie,YANG Yuexiang. Research on post-pruning algorithm of decision tree[J]. Journal of Hunan Radio and Television University, 2005(1): 54-56.(in Chinese)
[16] 田启华, 黄超, 于海东, 等. 基于AHP的耦合任务集资源分配权重确定方法[J]. 计算机工程与应用, 2018, 54(21): 25-30,94.
TIAN Qihua, HUANG Chao, YU Haidong, et al. Approach for determining weight of resource allocation in coupled task set based on AHP[J]. Computer Enginee-ring and Applications, 2018, 54(21): 25-30,94.(in Chinese)
[17] 任莹晖, 黄向明, 马忠凯, 等. 基于信息熵的物料配送时间节点预测方法[J]. 中国机械工程, 2018(22): 1-7.
REN Yinghui, HUANG Xiangming, MA Zhongkai, et al. Time node prediction method for material delivery based on inforemation entropy[J]. China Mechanical Engineering, 2018(22): 1-7.(in Chinese)

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备注/Memo

备注/Memo:
Received:2018-11-15;Accepted:2019-03-19
Foundation:Science and Technology Project of Sichuan Province(2017FZ0094);Science and Technology Project of Chengdu(2017-RK00-00021-ZF);Information and Communication Project of State Grid Sichuan Electric Power Company Information and Communication Corporation(SGSCXT00XGJS1800219)
Corresponding author:Professor WANG Diangang.E-mail: wang_dg@qq.com
Citation:WANG Diangang, HUANG Lin, CHANG Jian,et al.Load forecasting model based on ARIMA and CART[J]. Journal of Shenzhen University Science and Engineering, 2019, 36(3): 245-251.(in Chinese)
基金项目:四川省科技计划资助项目(2017FZ0094);成都市科技资助项目(2017-RK00-00021-ZF);国网四川省电力公司信息通信公司资助项目(SGSCXT00XGJS1800219)
作者简介:王电钢(1973— ),国网四川省电力公司信息通信公司教授级高级工程师. 研究方向:电力信息化.E-mail:wang_dg@qq.com
引文:王电钢,黄林,常健,等.基于ARIMA和CART的负载预测模型[J]. 深圳大学学报理工版,2019,36(3):245-251.
更新日期/Last Update: 2019-04-22