Abstract
Finding similar time series is an important task in multimedia retrieval, including motion gesture recognition, speech recognition, or classification of hand-written letters. These applications typically require the similarity (or distance) measure to be robust against outliers and time warps. Time warps occur if two time series follow the same path in space, but need specific time adjustments. A common distance measure respecting time warps is the dynamic time warping (\(\texttt {DTW}\)) function. The edit distance with real penalties (\(\texttt {ERP}\)) and the dog-keeper distance (\(\texttt {DK}\)) are variations of \(\texttt {DTW}\) satisfying the triangle inequality. In this paper we propose a novel extension of the \(\texttt {DK}\) distance called windowed dog-keeper distance (\(\texttt {WDK}\)). It operates on sliding windows, which makes it robust against outliers. It also satisfies the triangle inequality from the \(\texttt {DK}\) distance. We experimentally compare our measure to the existing ones and discuss the conditions under which it shows an optimal classification accuracy. Our evaluation also contributes a comparison of \(\texttt {DK}{}\) and \(\texttt {DTW}{}\). For our experiments, we use well-known data sets such as the cylinder-bell-funnel data set and data sets from the UCI Machine Learning Repository.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Backurs, A., Indyk, P.: Edit distance cannot be computed in strongly subquadratic time (unless SETH is false). CoRR, abs/1412.0348 (2014)
Bringmann, K.: Why walking the dog takes time: Frechet distance has no strongly subquadratic algorithms unless SETH fails. CoRR, abs/1404.1448 (2014)
Bringmann, K., Künnemann, M.: Quadratic conditional lower bounds for string problems and dynamic time warping. CoRR, abs/1502.01063 (2015)
Chen, L., Ng, R.: On the marriage of Lp-norms and edit distance. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases, VLDB 2004, vol. 30, pp. 792–803. VLDB Endowment (2004)
Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and mining of time series data: experimental comparison of representations and distance measures. Proc. VLDB Endow. 1(2), 1542–1552 (2008)
Eiter, T., Mannila, H.: Computing discrete Fréchet distance. Technical report, Technische Universität Wien (1994)
René Fréchet, M.: Sur quelques points du calcul fonctionnel. 22. Rendiconti del Circolo Mathematico di Palermo (1906)
Kadous, M.W.: Temporal classification: extending the classification paradigm to multivariate time series. Ph.D. thesis, School of Computer Science and Engineering, University of New South Wales (2002)
Lichman, M.: UCI machine learning repository (2013)
Lin, J., Keogh, E., Wei, L., Lonardi, S.: Experiencing SAX: a novel symbolic representation of time series. Data Min. Knowl. Discov. 15(2), 107–144 (2007)
Sakoe, H., Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. In: Waibel, A., Lee, K. (eds.) Readings in Speech Recognition, pp. 159–165. Morgan Kaufmann Publishers Inc., San Francisco (1990)
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
Bachmann, J.P., Freytag, JC. (2017). Dynamic Time Warping and the (Windowed) Dog-Keeper Distance. In: Beecks, C., Borutta, F., Kröger, P., Seidl, T. (eds) Similarity Search and Applications. SISAP 2017. Lecture Notes in Computer Science(), vol 10609. Springer, Cham. https://doi.org/10.1007/978-3-319-68474-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-68474-1_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68473-4
Online ISBN: 978-3-319-68474-1
eBook Packages: Computer ScienceComputer Science (R0)