基于混沌时间序列预测的多源数据转换算法设计
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摘要
在对多源数据进行转换时传统方法未考虑多源数据的混沌特性,导致进行多源数据转换时选取的转换频率点缺乏针对性、与多源数据间的关联性低,出现了多源数据转换准确性低、效率低等问题,提出基于基于混沌时间序列预测的多源数据转换算法。连接原始时间序列和多维空间序列,重构多源数据相空间,设置合理的相空间嵌入维数以及混沌时间延迟,在该相空间中提取混沌时间序列的关联维特征,依据该特征预测混沌时间序列,根据预测结果选取数据转换的转换频率点,最终实现多源数据的准确转换。实验结果证明,使用所提方法进行多源数据转换,效率较高,所得到的转换数据准确率较高。
The traditional methods do not consider the chaotic characteristics of multi-source data when converting multisource data,which leads to the lack of pertinence of the selected conversion frequency points and the low correlation with multi-source data.The problems of low accuracy and low efficiency of multi-source data conversion appear.This paper proposes a chaotic time series based method.Multi source data conversion algorithm for column prediction.Connecting the original time series and multi-dimensional space series,reconstructing the phase space of multi-source data,setting a reasonable embedding dimension of the phase space and chaotic time delay,extracting the correlation dimension features of chaotic time series in the phase space,predicting the chaotic time series according to the features,and selecting the conversion frequency of data conversion according to the prediction results.Finally,the accurate conversion of multisource data is realized.The experimental results show that the proposed method has high efficiency and high accuracy in multi-source data conversion.
The traditional methods do not consider the chaotic characteristics of multi-source data when converting multisource data,which leads to the lack of pertinence of the selected conversion frequency points and the low correlation with multi-source data.The problems of low accuracy and low efficiency of multi-source data conversion appear.This paper proposes a chaotic time series based method.Multi source data conversion algorithm for column prediction.Connecting the original time series and multi-dimensional space series,reconstructing the phase space of multi-source data,setting a reasonable embedding dimension of the phase space and chaotic time delay,extracting the correlation dimension features of chaotic time series in the phase space,predicting the chaotic time series according to the features,and selecting the conversion frequency of data conversion according to the prediction results.Finally,the accurate conversion of multisource data is realized.The experimental results show that the proposed method has high efficiency and high accuracy in multi-source data conversion.
引文
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[3]赵振营.大数据中资源科技信息定位快速查询仿真[J].计算机仿真,2017,34(8):417-420.
[4]姜明月.云计算平台下的大数据分流系统的设计与优化[J].现代电子技术,2016,39(2):28-3.
[5]曾汪旺,谢颖夫,胡光阔.多源异构数据整合系统在医疗大数据中的应用[J].价值工程,2017,36(8):80-82.
[6]黄递全,向娟,田寿全.多源传感器数据采集系统的设计与实现[J].地理空间信息,2016,14(1):20-22.
[7]孟海东,任敬佩.基于云计算平台的聚类算法[J].计算机工程与设计,2015(11):2990-2994.
[8]刘洋,刘博,王峰.基于Parameter Server框架的大数据挖掘优化算法[J].山东大学学报(工学版),2017,47(4):1-6.
[9]何军.基于云计算技术的大数据下企业危机管理系统研究[J].科技管理研究,2014,34(21):159-164.
[10]王欣,周晓梅.云计算环境下大数据合理分流技术研究与仿真[J].计算机仿真,2016,33(3):292-295.
[11]叶春森,梁昌勇,梁雯.基于云计算-大数据的价值链创新机制研究[J].科技进步与对策,2014,31(24):13-17.
[12]安志强.多源风场数据集成分析系统的设计[J].电子技术与软件工程,2016(7):66-67.
[13]涂俊英.云计算下多源异构大数据跨源调度算法[J].科学技术与工程,2017(34):268-272.
[14]ARUL XAVIER V M,ANNADURAI S.Chaotic social spider algorithm for load balance aware task scheduling in cloud computing[J].Cluster Computing,2018(9):1-11.
[15]IYENGAR N C S N,GANAPATHY G.Chaotic theory based defensive mechanism against distributed denial of service attack in cloud computing environment[J].International Journal of Security&Its Applications,2015,9(9):197-212.