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DTW距离的过滤搜索方法 被引量:3

Filtering search method for DTW distance
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摘要 动态时间弯曲(DTW)距离支持时间序列的多种形变,具有较高的匹配精度,是一种重要的相似性度量方法.然而,该方法计算复杂度较高,制约了其在相似性搜索中的应用.为了平衡匹配精度与计算效率之间的矛盾,提出一种过滤搜索方法.首先,构造一种计算代价较低的DTW下界距离,用其进行粗略过滤,得到候选集;然后,利用提前终止策略,优化计算候选集中序列的DTW距离,得到搜索结果;最后,对所提出方法进行实验验证,结果表明,该方法能够提高DTW距离的相似性搜索效率,且具有非漏报性. Dynamic time warping(DTW) is an important similarity measure method, which supports a variety of deformation of time series and has high matching precision. However, the method has high computational complexity,which restricts its application in similarity search. In order to balance the contradiction between the matching precision and the computational efficiency, a filtering search method is proposed. Firstly, a lower-bounding distance for DTW is constructed, and it is used in the filtering search to obtain the candidate set. Then, the early abandon strategy is used in the candidate set to achieve the search results. Finally, the proposed method is verified by experiments. The results show that it can improve the similarity search efficiency under DTW and guarantee no false dismissal.
作者 李正欣 郭建胜 王瑛 田舢 张晓丰 李超 LI Zheng-xin;GUO Jian-sheng;WANG Ying;TIAN Shan;ZHANG Xiao-feng;LI Chao(College of Equipment Management and UAV Engineering, Air Force Engineering University, Xi' an 710051, China;Center for OPTical IMagery Analysis and Learning(OPTIMAL), Northwestern Polytechnical University, Xi'an 710072, China)
出处 《控制与决策》 EI CSCD 北大核心 2018年第7期1277-1281,共5页 Control and Decision
基金 国家自然科学基金项目(61502521 71601183)
关键词 时间序列 相似性搜索 动态时间弯曲 提前终止 过滤搜索 time series similarity search dynamic time warping early abandon filtering search
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  • 1张明波,陆锋,申排伟,程昌秀.R树家族的演变和发展[J].计算机学报,2005,28(3):289-300. 被引量:95
  • 2MARSZA EK A, BURCZY SKI T. Modeling and forecasting financial time series with ordered fuzzy candlesticks[J]. Information sciences, 2014, 273: 144-155.
  • 3ZAMORA M, LAMBERT A, MONTERO G. Effect of some meteorological phenomena on the wind potential of Baja California[J]. Energy procedia, 2014, 57: 1327-1336.
  • 4GRAVIO G D, MANCINI M, PATRIARCA R, et al. Overall safety performance of air traffic management system: forecasting and monitoring[J]. Safety science, 2015, 72: 351-362.
  • 5SAKOE H, CHIBA S. Dynamic programming algorithm optimization for spoken word recognition[J]. IEEE transactions on acoustics, speech, and signal processing, 1978, 26(1): 43-49.
  • 6IZAKIAN H, PEDRYCZ W, JAMAL I. Fuzzy clustering of time series data using dynamic time warping distance[J]. Engineering applications of artificial intelligence, 2015, 39: 235-244.
  • 7ZHANG Zheng, TANG Ping, DUAN Rubing. Dynamic time warping under pointwise shape context[J]. Information sciences, 2015, 315: 88-101.
  • 8LI Hailin. Asynchronism-based principal component analysis for time series data mining[J]. Expert systems with applications, 2014, 41(6): 2842-2850.
  • 9KEOGH E, PAZZANI M J. Derivative dynamic time warping[C]//Proceedings of the 1st SIAM International Conference on Data Mining. Chicago, IL, USA, 2001: 1-11.
  • 10JEONG Y S, JAYARAMAN R. Support vector-based algorithms with weighted dynamic time warping kernel function for time series classification[J]. Knowledge-based systems, 2015, 75: 184-191.

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