Computer Science ›› 2017, Vol. 44 ›› Issue (1): 247-252.doi: 10.11896/j.issn.1002-137X.2017.01.046

Previous Articles     Next Articles

Similarity Measure Algorithm of Time Series Based on Binary-dividing SAX

ZHANG Jian-hui, WANG Hui-qing, SUN Hong-wei, GUO Zhi-rong and BAI Ying-ying   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Time series dimentionality reduction technology is used to resolve high dimensionality time series.Symbolic aggregate appro ximation (SAX representation) is a time series dimensionality reduction technique which benefits from its brief representation in dimensionality reduction and highperformance lower bound distance algorithm,but there is a question that the number of segments,a parameter in SAX,is set artificially based on the characteristic of individual time series.To solve this problem,similarity measure algorithm of time series based on binarydividing SAX was presented by introducing sliding window and statistical methods.The experimental results show that binarydividing SAX algorithm not only solves the difficulty to choose the number of segments,but also reduces the complexity of time series representation in dimensionality reduction and improves classification accuracy by using the SAX algorithm in a variety of time series data.

Key words: Dimensionality reduction,Symbolic aggregate approximation,Sliding window

[1] LI Hai-lin,Yang Li-bin.Method of dimensionality reduction and feature representation for time series [J].Contorl and Decision,2013 (11):1718-1722.(in Chinese) 李海林,杨丽彬.时间序列数据降维和特征表示方法[J].控制与决策,2013 (11):1718-1722.
[2] DAW C S,FINNEY C E A,TRACY E R.A review of symbolic analysis of experimental data[J].Review of Scientific Instruments,2003,74(2):915-930.
[4] LIN J,KEOGH E,WEI L,et al.Experiencing SAX:a novelsymbolic representation of time series[J].Data Mining & Knowledge Discovery,2007,15(2):107-144.
[5] SHIEH J,KEOGH E.i SAX:indexing and mining Terabytesized time series[C]∥Proceedings of the14th ACM SIGKDD international Conference on Knowledge Discovery and Data Mi-ning.ACM,2008:623-631.
[6] CAMERRA A,PALPANAS T,SHIEH J,et al.iSAX 2.0:Indexing and Mining One Billion Time Series[C]∥2013 IEEE 13th International Conference on Data Mining.IEEE,2010:58-67.
[8] LKHAGVA B,SUZUKI Y,KAWAGOE K.Extended SAX:Ex-tension of symbolic aggregate approximation for financialtime series data representation[C]∥DEWS 2006.2006.
[9] HE X,SHAO C,XIONG Y.A non-parametric symbolic approximate representation for long time series[J].Formal Pattern Analysis & Applications,2016,19(1):111-127.
[10] SUN Y,LI J,LIU J,et al.An improvement of symbolic aggregate approximation distance measure for time series[J].Neurocomputing,2014,8(11):189-198.
[11] FUAD M M M,MARTEAU P F.Towards a faster symbolic aggregate approximation method[C]∥ICSOFT 2010.2010.
[13] BARNAGHI P,GANZ F,HENSON C,et al.Computing perception from sensor data[C]∥Sensors,2012 IEEE.IEEE,2012:1-4.
[15] BURANASING A,PRAYOTE A.Storm intensity estimation using symbolic aggregate approximation and artificial neural network[C]∥International Computer Science and Engineering Conference.2014.
[16] KEOGH E,ZHU Q,HU B,et al.Ratanamahatana,The UCR time seriesclassification/clustering homepage.

No related articles found!
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .
[10] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99, 116 .