[1]汤其婕,朱小萍.基于关键点的不确定时间序列线性降维方法[J].计算机技术与发展,2018,28(08):22-26.[doi:10.3969/ j. issn.1673-629X.2018.08.005]
　TANG Qi-jie,ZHU Xiao-ping.A Linear Dimensionality Reduction Method Based on Key Points for Uncertain Time Series[J].,2018,28(08):22-26.[doi:10.3969/ j. issn.1673-629X.2018.08.005]
点击复制

基于关键点的不确定时间序列线性降维方法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年08期
页码:
22-26
栏目:
智能、算法、系统工程
出版日期:
2018-08-10

文章信息/Info

Title:
A Linear Dimensionality Reduction Method Based on Key Points for Uncertain Time Series
文章编号:
1673-629X(2018)08-0022-05
作者:
汤其婕; 朱小萍
南京航空航天大学 计算机科学与技术学院,江苏 南京 211106
Author(s):
TANG Qi-jie; ZHU Xiao-ping
School of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
关键词:
不确定时间序列; 描述统计模型; 关键点; 线性降维
Keywords:
uncertain time series; descriptive statistical model; key points; linear dimensionality reduction
分类号:
TP311
DOI:
10.3969/ j. issn.1673-629X.2018.08.005
文献标志码:
A
摘要:
与确定时间序列相比,不确定时间序列在每个时间点上的取值不是一个确定的值,而是一个可能值的集合,这种不确定给时间数据的降维处理带来了巨大的挑战。 加之时间序列固有的数据规模大、数据维度高的特点,对不确定时间序列进行预处理必不可少,现有的针对确定时间序列的降维方法已经不再适用。 为解决此问题,建立适当的数据描述统计模型,将原始不确定时间序列归约为三条确定时间序列。 同时,针对该模型,提出基于关键点的不确定时序数据线性降维算法。 该算法综合考虑体现时序数据特征的极值点与转折点,在进行高效数据降维的同时避免了过度除噪的弊端。 实验结果表明,该描述统计模型与基于关键点的线性降维算法的结合具有良好的降维效果,且对于不同领域的数据具有较好的普适性。
Abstract:
Compared with traditional time series,the value of uncertain time series at each timestamp is a set of many possible values,which brings great challenges to linear dimensionality reduction for uncertain time series. Considering that uncertain time series data is large-scaled and multidimensional,it is necessary to preprocess raw data before proceeding to the next step. Traditional methods for uncertain time series dimensionality reduction are no longer applicable. To deal with the problem,we propose a descriptive statistical modelwhich reduces the origin uncertain time series into three certain time series. In addition,a new time series data segmentation algorithm is proposed based on the model. The algorithm takes both extreme point and turning point into consideration,which makes efficient data dimensionality reduction while avoiding excessive noise cancellation. Experiment shows that the combination of linear dimensionality reduction method and statistical model has a great effect on dimensionality reduction. Furthermore,the method is also universal for data in different fields.

相似文献/References:

[1]李成为,王 屿,郑迪威.基于 MR 框架的不确定时间序列相似性计算方法[J].计算机技术与发展,2018,28(10):27.[doi:10.3969/ j. issn.1673-629X.2018.10.006]
　LI Cheng-wei,WANG Yu,ZHENG Di-wei.A Similarity Computation Method of Uncertain Time Series Based on MR Framework[J].,2018,28(08):27.[doi:10.3969/ j. issn.1673-629X.2018.10.006]
[2]汤其婕,王玙.基于I-GARCH的不确定时间序列概率分布推算[J].计算机技术与发展,2018,28(12):23.[doi:10．3969/j． issn．1673－629X．2018．12．005]
　TANG Qijie,WANG Yu.Probability Distribution Estimation of Uncertain Time Series Based on I－GARCH[J].,2018,28(08):23.[doi:10．3969/j． issn．1673－629X．2018．12．005]
[3]王 玙,左良利.关系数据库支持的不确定时间序列存储[J].计算机技术与发展,2019,29(11):7.[doi:10. 3969 / j. issn. 1673-629X. 2019. 11. 002]
　WANG Yu,ZUO Liang-li.Research on Storage of Uncertain Time Series in Relational Databases[J].,2019,29(08):7.[doi:10. 3969 / j. issn. 1673-629X. 2019. 11. 002]

更新日期/Last Update: 2018-10-15