The Influence of Global Constraints on DTW and LCS Similarity Measures for Time-Series Databases

  • Vladimir Kurbalija
  • Miloš Radovanović
  • Zoltan Geler
  • Mirjana Ivanović
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 101)


Analysis of time series represents an important tool in many application areas. A vital component in many types of time-series analysis is the choice of an appropriate distance/similarity measure. Numerous measures have been proposed to date, with the most successful ones based on dynamic programming. Being of quadratic time complexity, however, global constraints are often employed to limit the search space in the matrix during the dynamic programming procedure, in order to speed up computation. In this paper, we investigate two representative time-series distance/similarity measures based on dynamic programming, Dynamic Time Warping (DTW) and Longest Common Subsequence (LCS), and the effects of global constraints on them. Through extensive experiments on a large number of time-series data sets, we demonstrate how global constrains can significantly reduce the computation time of DTW and LCS. We also show that, if the constraint parameter is tight enough (less than 10–15% of time-series length), the constrained measure becomes significantly different from its unconstrained counterpart, in the sense of producing qualitatively different 1-nearest neighbour (1NN) graphs. This observation highlights the need for careful tuning of constraint parameters in order to achieve a good trade-off between speed and accuracy.


Dynamic Time Warping Global Constraint Longe Common Subsequence Longe Common Subsequence Elastic Measure 
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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Vladimir Kurbalija
    • 1
  • Miloš Radovanović
    • 1
  • Zoltan Geler
    • 2
  • Mirjana Ivanović
    • 1
  1. 1.Department of Mathematics and Informatics, Faculty of ScienceUniversity of Novi SadNovi SadSerbia
  2. 2.Faculty of PhilosophyUniversity of Novi SadNovi SadSerbia

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