{"title":"Adaptive Dynamic Time Warping for Variable Structure Pattern Recognition","authors":"S. V. Yendiyarov","volume":82,"journal":"International Journal of Mechanical and Industrial Engineering","pagesStart":1352,"pagesEnd":1357,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/17207","abstract":"<p>Pattern discovery from time series is of fundamental importance. Particularly, when information about the structure of a pattern is not complete, an algorithm to discover specific patterns or shapes automatically from the time series data is necessary. The dynamic time warping is a technique that allows local flexibility in aligning time series. Because of this, it is widely used in many fields such as science, medicine, industry, finance and others. However, a major problem of the dynamic time warping is that it is not able to work with structural changes of a pattern. This problem arises when the structure is influenced by noise, which is a common thing in practice for almost every application. This paper addresses this problem by means of developing a novel technique called adaptive dynamic time warping.<\/p>\r\n","references":"[1] D. Berndt, J. Clifford.: Using Dynamic Time Warping to Find Patterns \r\nin Time Series. In KDD-94: AAAI Workshop on Knowledge Discovery \r\nin Databases, Seattle, Washington, July 1994, pp. 359-370. \r\n[2] K. Hiri-o-tappa, S. Narupiti, S. Pattara-atikom.: A novel approach of \r\ndynamic time warping for short-term traffic congestion prediction, \r\nTransportation Research Board 90th Annual Meeting, January 23-27, \r\nWashington, D.C., 2011, pp. 1-15. \r\n[3] E. Keogh and C. Ratanamahatana.: Exact indexing of dynamic time \r\nwarping, Knowl. Inf. Syst. 7(3), 2005, pp. 358-386. \r\n[4] Y. Sakurai.: FTW: fast similarity search under the time warping \r\ndistance, PODS, 2005, pp. 326-337. \r\n[5] A. Fu, E. Keogh, L. Lau, C. Ratanamahatana, R. Wong.: Scaling and \r\ntime warping in time series querying, VLDB, 17, 4, pp. 899-921, 2008. \r\n[6] N. Gillian, R. Knapp, S. O\u2019Modhrain.: Recognition of multivariate \r\ntemporal musical gestures using n-dimensional dynamic time warping. \r\nProc of the 11th Int'l conference on New Interfaces for Musical \r\nExpression, 2011. \r\n[7] E. Keogh, L. Wei, X. Xi, M. Vlachos, S.H. Lee, P. Protopapas.: \r\nSupporting exact indexing of arbitrarily rotated shapes and periodic time \r\nseries under Euclidean and warping distance measures. VLDB J. 18, 3, \r\npp. 611-630, 2009. \r\n[8] A. Mueen, E. Keogh.: Online discovery and maintenance of time series \r\nmotifs. KDD, pp. 1089-1098, 2010. \r\n[9] L. Ye, E. Keogh.: Time series shapelets: a new primitive for data mining. \r\nKDD, pp. 947-956, 2009. \r\n[10] Y. Zhang, J. Glass.: An inner-product lower-bound estimate for dynamic \r\ntime warping. ICASSP, pp. 5660-5663, 2011. \r\n[11] E. Keogh.: Exact indexing of dynamic time warping.In VLDB, pp. 406\u2013\r\n417, 2002. \r\n[12] E. Keogh, M. Pazzani.: Scaling up dynamic time warping for data \r\nmining applications, KDD 2000, pp. 285-289, 2000. \r\n[13] B. Yi.: Efficient Retrieval of Similar Time Sequences Under Time \r\nWarping, ICDE 1998, pp. 201-208, 1998. \r\n[14] Yendiyarov, S., Zobnin B., Petrushenko S. Expert system for sintering \r\nprocess control based on the information about solid-fuel flow composition \/\/ Proceedings of World Academy of Science, Engineering \r\nand Technology, France, Issue 68, August 2012, pp. 861-868 ","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 82, 2013"}