全部 标题 作者
关键词 摘要


基于统计极值的流程对象时间序列时序计算算法
A Novel Timing Calculation Algorithm Based on Statistical Extremum for the Time Series of Process Object

DOI: 10.12677/HJDM.2016.64020, PP. 179-191

Keywords: 流程对象,数据挖掘,时间序列,统计,极值,延迟
Process Object
, Data Mining, Time Series, Statistics, Extreme Value, Delay

Full-Text   Cite this paper   Add to My Lib

Abstract:

本文针对流程对象采样数据集,提出了一种基于统计极值的流程对象环节间时序计算算法,同时通过理论分析证明了该算法的正确性。该算法通过取数据的特征点,计算环节间特征点的时间距,并通过统计方法,计算出流程对象任意两环节间的延迟时间,进而得到多环节间的时序关系。通过实际流程工业采样数据集测试,可基本准确的求得任意环节数据之间的延迟时间距以及各环节间的时序关系。
In this paper, an algorithm for computing timing relationship among each link of the process object is proposed, and the validity of the algorithm is proved through the theoretical analysis. The algorithm is designed based on statistical time distance among extremum points of sampling data set of the process industry, can calculate the delay time between any two time series, and then get timing relationship between any two links. At the same time, experiments with sampling data set of the process industry demonstrates that the algorithm can obtain the delay time interval among time series and the timing relationship between each link of process object.

References

[1]  Sakurai, Y., Papadimitriou, S. and Faloutsos, C. (2005) Braid: Stream Mining through Group Lag Correlations. Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, Baltimore, 14-16 June 2005, 599-610.
http://dx.doi.org/10.1145/1066157.1066226
[2]  林子雨, 江弋, 赖永炫, 林琛. 一种新的时间序列延迟相关性分析算法——三点预测探查法[J]. 计算机研究与发展, 2012(12): 2645-2655.
[3]  Yue, D., Zhang, T., Yu, G., et al. (2007) Lag Correlation Analysis Based on Boolean Presentation over Multiple Data Streams. International Conference on Intelligent Systems and Knowledge Engineering. Atlantis Press, Paris.
http://dx.doi.org/10.2991/iske.2007.133
[4]  Zhang, T., Yue, D., Wang, Y., et al. (2011) A Novel Approach for Mining Multiple Data Streams Based on Lag Correlation. 2011 Chinese Control and Decision Conference (CCDC), Mianyang, 23-25 May 2011, 2377-2382.
http://dx.doi.org/10.1109/CCDC.2011.5968606
[5]  Fungwacharakorn, W. and Pattara-Atikom, W. (2014) Enhancement of Lag Time Query on Hydrologic Data Using Clipping Technique and Logic-Based Correlation. 2014 11th International Conference on Electrical Engineering/ Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Nakhon Ratchasima, 14-17 May 2014, 1-6.
http://dx.doi.org/10.1109/ECTICon.2014.6839813
[6]  武红江, 赵军平, 彭勤科, 黄永宣. 基于波动特征的时间序列数据挖掘[J]. 控制与决策, 2007, 22(2): 160-163.
[7]  谢福鼎, 王赫楠, 张永. 一种新的时间序列线性拟合方法[J]. 计算机工程, 2011, 37(22): 250-251+254.
[8]  李海林. 基于动态弯曲的时间序列异步相关性分析[J]. 计算机应用研究, 2014, 31(7): 1976-1979.
[9]  Serra, J. and Arcos, J.L. (2012) A Competitive Measure to Assess the Similarity between Two Time Series. Case- Based Reasoning Research and Development. Springer, Berlin Heidelberg, 414-427.
http://dx.doi.org/10.1007/978-3-642-32986-9_31
[10]  Stefan, A., Athitsos, V. and Das, G. (2013) The Move-Split-Merge Metric for Time Series. IEEE Transactions on Knowledge and Data Engineering, 25, 1425-1438.
http://dx.doi.org/10.1109/TKDE.2012.88
[11]  Nakamura, T., Taki, K., Nomiya, H., et al. (2013) A Shape-Based Similarity Measure for Time Series Data with Ensemble Learning. Pattern Analysis and Applications, 16, 535-548.
http://dx.doi.org/10.1007/s10044-011-0262-6
[12]  Boucheham, B. (2010) Reduced Data Similarity-Based Matching for Time Series Patterns Alignment. Pattern Recognition Letters, 31, 629-638.
http://dx.doi.org/10.1016/j.patrec.2009.11.019
[13]  Li, H., Guo, C. and Qiu, W. (2011) Similarity Measure Based on Piecewise Linear Approximation and Derivative Dynamic Time Warping for Time Series Mining. Expert Systems with Applications, 38, 14732-14743.
http://dx.doi.org/10.1016/j.eswa.2011.05.007
[14]  丁永伟, 杨小虎, 陈根才, Kavs, A.J. 基于弧度距离的时间序列相似度量[J]. 电子与信息学报, 2011, 33(1): 122- 128.
[15]  肖瑞, 刘国华. 基于趋势的时间序列相似性度量和聚类研究[J]. 计算机应用研究, 2014, 31(9): 2600-2605.
[16]  Song, Q., Guo, Q., Wang, K., et al. (2014) A Scheme for Mining State Association Rules of Process Object Based on Big Dat. Journal of Computer and Communications, 2, 17-24.
http://dx.doi.org/10.4236/jcc.2014.214002

Full-Text

comments powered by Disqus