Flexible Dynamic Time Warping for Time Series Classification

Abstract Measuring the similarity or distance between two time series sequences is critical for the classification of a set of time series sequences. Given two time series sequences, X and Y , the dynamic time warping (DTW) algorithm can calculate the distance between X and Y . But the DTW algorithm may align some neighboring points in X to the corresponding points which are far apart in Y . It may get the alignment with higher score, but with less representative information. This paper proposes the flexible dynamic time wrapping (FDTW) method for measuring the similarity of two time series sequences. The FDTW algorithm adds an additional score as the reward for the contiguously long one-to-one fragment. As the experimental results show, the DTW and DDTW and FDTW methods outperforms each other in some testing sets. By combining the FDTW, DTW and DDTW methods to form a classifier ensemble with the voting scheme, it has less average error rate than that of each individual method.

[1]  L. Gupta,et al.  Nonlinear alignment and averaging for estimating the evoked potential , 1996, IEEE Transactions on Biomedical Engineering.

[2]  Eamonn J. Keogh,et al.  On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration , 2002, Data Mining and Knowledge Discovery.

[3]  Daniel S. Hirschberg,et al.  A linear space algorithm for computing maximal common subsequences , 1975, Commun. ACM.

[4]  Tomasz Górecki,et al.  Using derivatives in a longest common subsequence dissimilarity measure for time series classification , 2014, Pattern Recognit. Lett..

[5]  Hsing-Yen Ann,et al.  Efficient polynomial-time algorithms for the constrained LCS problem with strings exclusion , 2014, J. Comb. Optim..

[6]  Hsing-Yen Ann,et al.  Efficient algorithms for finding interleaving relationship between sequences , 2008, Inf. Process. Lett..

[7]  Eamonn J. Keogh,et al.  Derivative Dynamic Time Warping , 2001, SDM.

[8]  Olufemi A. Omitaomu,et al.  Weighted dynamic time warping for time series classification , 2011, Pattern Recognit..

[9]  Hsing-Yen Ann,et al.  Efficient Algorithms for the Longest Common Subsequence Problem with Sequential Substring Constraints , 2011, 2011 IEEE 11th International Conference on Bioinformatics and Bioengineering.

[10]  Chang-Biau Yang,et al.  Efficient Algorithms for the Flexible Longest Common Subsequence Problem , 2014 .

[11]  Richard C. T. Lee,et al.  Systolic algorithms for the longest common subsequence problem , 1987 .