A Relational Analysis Approach of Time Series with Uncertain Information

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A Relational Analysis Approach of Time Series with Uncertain Information

更新时间：2025-07-09

- A Relational Analysis Approach of Time Series with Uncertain Information
- Vol. 44, Issue 6, Pages: 67-71+98(2010)
- 作者机构：
  
  空军工程大学工程学院,西安,710038
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP301.6
- Online First：10 June 2010，
  
  Published：2010
- 稿件说明：
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A Relational Analysis Approach of Time Series with Uncertain Information[J]. 2010, 44(6): 67-71+98.
DOI：

A Relational Analysis Approach of Time Series with Uncertain Information[J]. 2010, 44(6): 67-71+98. DOI：

摘要

针对不确定信息环境中无法直接计算序列关联度的问题

提出了区间数序列关联趋势分析法(RTASI法).首先利用区间数距离表示不确定信息的差别

经过时移和翻转转换对序列进行匹配后

通过区间数序列之间的距离计算序列关联度

最后应用集对分析法对序列间的关联趋势进行分类.RTASI法将关联度计算的范围推广到不确定信息环境下

并给出序列关联趋势的分类结果.实验结果表明

RTASI法的分类准确率较高

其虚警率和漏警率均低于C均值法和模拟退火法

运行时间分别比C均值法和模拟退火法减少了77%和63.4%.

Abstract

In view of the deficiency to directly calculate the relational degree between time series of uncertain information

an approach to analyze the relational trend between series of interval numbers is proposed. The approach quantifies the difference between uncertain information using the distance of interval numbers

and matches the series through time shifting and inversion transformation. Then the relational degree of time series is calculated based on the distance between time series of interval numbers

and the relational trends of uncertain information series are classified through the method of set pair analysis. The approach extends the application range of relational degree from crisp numbers to uncertain environment expressed by interval numbers

and classifies the degree of relational trend between time series. Comparisons with both the C-means algorithm and the simulation anneal algorithm show that the accuracy of the proposed algorithm is higher; the false alarm rate and the missing alarm rate are both lower; and the running time is reduced by 77% and 63.4% respectively.

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references

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