Abstract
Time series data are found everywhere in the real world and their analysis is needed in many practical situations. Multivariate time series data poses problem for analysis due to its dynamic nature and traditional machine learning algorithms for static data become unsuitable for direct application. A measure to assess the similarity of two time series is essential in time series processing and a lot of measures have been developed. In this work a comparative study of some of the most popular similarity measures has been done with 43 benchmark data set from UCR time series repository. It has been found that, on the average over the different data sets, DTW performs better in terms of classification accuracy but it has high computational cost. A simple processing technique for reducing the data set to lower computational cost without much degradation in classification accuracy is proposed and studied. A new similarity measure is also proposed and its efficiency is examined compared to other measures.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kantz, H., Schreiber, T.: Nonlinear Time Series Analysis. Cambridge University Press, Cambridge (2004)
Buza, K., Nanopoulos, A., Schmidt-Thieme, L.: Time series classification based on individual error prediction. In: IEEE International Conference on Computational Science and Engineering (2010)
Chakraborty, B.: Feature selection and classification techniques for multivariate time series. In: Proceedings of ICICIC 2007, September 2007
Zhang, X., Wu, J., et al.: A novel pattern extraction method for time series classification. Optimiz. Engin. 10(2), 253–271 (2009)
Buza, K., Nanopoulos, A., Schmidt-Thieme, L.: Fusion of similarity measures for time series classification. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds.) HAIS 2011. LNCS (LNAI), vol. 6679, pp. 253–261. Springer, Heidelberg (2011). doi:10.1007/978-3-642-21222-2_31
Chakraborty, B.: A proposal for classification of multi sensor time series databased on time delay embedding. In: Proceedings of 8th International Conference on Sensing Technology ICST, Liverpool, England, pp. 31–35, September 2014
Chakraborty, B., Manabe, Y.: An efficient approach for person authentication using online signature verification. In: Proceedings of SKIMA 2008, pp. 12–17 (2008)
Keogh, E., Zhu, Q., Hu, B., Hao, Y., Xi, X., Wei, L., Ratanamahatana, C.A.: The UCR Time Series Classification/Clustering (2011). www.cs.ucr.edu/~eamonn/time_series_data/
Serra, J., Arcios, J.: An empirical evaluation of similarity measures for time series classification. Knowl. Based Syst. 67, 305–314 (2014)
Giusti, R., Batista, G.: An empirical comparison of dissimilarity measures for time series classification. In: Brazilian Conference on Intelligent Systems (BRACIS 2013) (2013)
Xing, Z., Pei, J., Keogh, E.: A brief survey on sequence classification. ACM SIGKDD Explor. Newslett. 12(1), 40–48 (2010)
Lal, T.N., et al.: Support vector channel selection in BCI. IEEE Trans. Biomed. Eng. 51(6), 1003–1010 (2004)
Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: Proceedings of ACM SIGKDD International Conference of Knowledge Discovery and Data Mining (2009)
Yoon, H., Yang, K., Sahabi, C.: Feature subset selection and feature ranking for multivariate time series. IEEE Trans. Knowl. Data Eng. 17(9), 1186–1198 (2005)
Kim, S.B., Han, K.S., et al.: Some effective techniques for naive bayes text classification. IEEE Trans. Knowl. Data Eng. 18(11), 1457–1466 (2006)
Wang, X., Mueen, A., Ding, H., Trajcevski, G., Scheuermann, P., Keogh, E.: Experimental comparison of representation methods and distance measures for time series data. Data Min. Knowl. Discov. 26, 275–309 (2013)
Levenshtein, I.: Binary codes capable of correcting deletions, inssertions and reversals. Sov. Phys. Dokl. 10, 707–710 (1966)
Marteau, F.: Time warp edit distance with stiffness adjustment for time series matching. IEEE Trans. PAMI 31, 306–318 (2009)
Alligood, K., Sauer, T., Yorke, J.A.: Chaos: An Introduction to Dynamical Systems. Springer, New York (1997)
Manabe, Y., Chakraborty, B.: Identity detection from online handwriting time series. In: Proceedings of SMCia08, pp. 365–370 (2007)
Aberbanel, H.D.I.: Analysis of Observed Chaotic Data’. Springer, New York (1996)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Yoshida, S., Chakraborty, B. (2017). A Comparative Study of Similarity Measures for Time Series Classification. In: Otake, M., Kurahashi, S., Ota, Y., Satoh, K., Bekki, D. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2015. Lecture Notes in Computer Science(), vol 10091. Springer, Cham. https://doi.org/10.1007/978-3-319-50953-2_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-50953-2_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50952-5
Online ISBN: 978-3-319-50953-2
eBook Packages: Computer ScienceComputer Science (R0)