信息不确定条件下时间序列的关联分析法

高亮 1; 孙卫 2; 3; 朱荣昌 1

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信息不确定条件下时间序列的关联分析法

更新时间：2025-07-09

- 信息不确定条件下时间序列的关联分析法
- A Relational Analysis Approach of Time Series with Uncertain Information
- 西安交通大学学报 2010年44卷第6期页码：67-71+98
- 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：
  
  : 2009-09-16. 作者简介: 高亮(1975-),男,博士生;朱荣昌(联系人),男,教授,博士生导师. 基金项目: 国家自然科学基金资助项目(10971164)
- DOI：
  中图分类号： TP301.6
- 网络首发：2010-06-10，
  
  纸质出版：2010
- 稿件说明：
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高亮 1, 孙卫 2, 3, 等. 信息不确定条件下时间序列的关联分析法[J]. 西安交通大学学报, 2010,44(6):67-71+98.

A Relational Analysis Approach of Time Series with Uncertain Information[J]. 2010, 44(6): 67-71+98.
高亮 1, 孙卫 2, 3, 等. 信息不确定条件下时间序列的关联分析法[J]. 西安交通大学学报, 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

MAURYA M R, RENGASWAM Y R, VENKATASUBRAMANIAN V. Fault diagnosis using dynamic trend analysis: a review and recent developments [J]. Engineering Applications of Artificial Intelligence, 2007, 20(2):133-146.

BALASKO B, BANKO Z, ABONYI J. Analyzing trends by symbolic episode representation and sequence alignment [C]∥Proc of 15th Mediterranean Conference on Control and Automation. Piscataway, NJ, USA: IEEE Press, 2007: 605-610.

DERELI T, DURMUSOGLU A. A trend-based patent alert system for technology watch [J]. Journal of Scientific Industrial Research, 2009, 68(8): 674-679.

MAURYA M R, RENGASWAMY R, VENKATASUBRAMANIAN V. A signed directed graph and qualitative trend analysis-based framework for incipient fault diagnosis [J]. Chemical Engineering Research Design, 2007,85(10): 1407-1422.

VILLEZ K, PELLETIER G, ROSEN C, et al. Comparison of two wavelet-based tools for data mining of urban water networks time series [J]. Water Science and Technology, 2007, 56(6): 57-64.

CHARBONNIER S, GARCIA-BELTAN C,CADET C. Trends extraction and analysis for complex system monitoring and decision support [J]. Engineering Applications of Artificial Intelligence, 2005, 18(1): 21-36.

ZHU Hongning, ZHANG Bin, GAO Xianwen. A service selection method of non-critical task in parallel structure [C]∥Proc of 2nd International Workshop on Knowledge Discovery and Data Mining. Piscataway, NJ, USA: IEEE Press, 2009: 846-850.

WONG Heung, HU Baoqing, IP W C, et al. Change-point analysis of hydrological time series using grey relational method [J]. Journal of Hydrology, 2006, 18(1): 323-338.

ZHU Jianjun, LIU Sifeng, LI Lihong. Research on four kinds of uncertain preference information aggregation approach in group decision making [J]. Kybernetes, 2009, 38(10): 1862-1869.

LI Xiaobai, SARKAR S. Against classification attacks: a decision tree pruning approach to privacy protection in data mining [J]. Operations Research, 2009, 57(6): 1496-1509.

LI Zhijun, ZHANG Lei, CHEN Mianyun, et al. Research and application of data mining method based on gray trend relational analysis [C]∥Proc of 2008 Pacifc-Asia Workshop on Computational Intelligence and Industrial Application. Piscataway, NJ, USA: IEEE Press, 2008: 98-101.

FU Mingfu, XIE Mingxiang, RAO Hong, et al. Fault diagnosis of suction fan based on the synthetically relational analysis [J]. China Mechanical Engineering, 2007, 18(20): 2403-2405.

SUDKAMP T. Examples, counterexamples, and measuring fuzzy associations [J]. Fuzzy Sets and Systems, 2005, 149(1):57-71.

温熙森.模式识别与状态监控[M].北京:科学出版社, 2007: 245-248.

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