[1]程艳云,张守超,杨杨. 基于大数据的时间序列预测研究与应用[J].计算机技术与发展,2016,26(06):175-178.
　CHENG Yan-yun,ZHANG Shou-chao,YANG Yang. Research and Application of Time Series Forecasting Based on Big Data[J].,2016,26(06):175-178.
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 基于大数据的时间序列预测研究与应用()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年06期
页码:
175-178
栏目:
应用开发研究
出版日期:
2016-06-10

文章信息/Info

Title:
 Research and Application of Time Series Forecasting Based on Big Data
文章编号:
1673-629X（2016)06-0175-04
作者:
 程艳云; 张守超; 杨杨
 南京邮电大学 自动化学院
Author(s):
 CHENG Yan-yun; ZHANG Shou-chao; YANG Yang
关键词:
 ; 大数据; 时间序列; 预测分析; 移动通信
Keywords:
 ; big data; time series; forecasting analysis; mobile communication
分类号:
TN915.07
文献标志码:
A
摘要:
 针对传统时间序列预测算法在分析海量数据时预测精度与预测速率低下的问题，提出一种全新的时间序列预测算法，研究如何将大数据技术应用到移动通信网时间序列形式的核心性能指标( KPI)预测中。文中首先介绍了移动通信网性能指标预测的意义及传统时间序列预测算法的缺陷。其次，基于移动通信网及时间序列特性，给出了基于大数据的时间序列预测算法的理论推导过程，通过大数据方法将时间序列分解为四个不同分量并进行特征提取，根据提取结果进行预测分析。最后，介绍了方法的实现过程，采用真实网络核心性能指标进行实验对比分析，验证该方法的可行性与效率。实验结果表明，基于大数据的时间序列预测算法相比于传统的时间序列预测算法，具有更高的预测精度、更快的预测速率。
Abstract:
 According to the detection accuracy and efficiency limitation of traditional time series forecasting methods when dealing with a large amount of data,a new time series forecasting method is put forward to study how to apply the big data technology into Key Perform-ance Index ( KPI) prediction of mobile communication network,which is form of time series. First,it introduces the significance of KPI prediction for mobile communication network and the defects of traditional time series prediction algorithm in this paper. Secondly,the theoretical derivation of time series prediction algorithm based on the big data is presented according to the characteristics of mobile com-munication network and time series. The time series is decomposed into four different components and the feature is extracted by the big data method,and the forecasting analysis is carried out according to the results of the extraction. Finally it gives implementation process and uses the real network KPI to carry out experimental comparative analysis for verification of the feasibility and efficiency of the big da-ta method. The experimental results show that the big data method has higher precision and rate compared with traditional methods.

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更新日期/Last Update: 2016-09-26