Time Alignment Measurement for Time Series

Duarte Folgado, Marília Barandas, Ricardo Matias, Rodrigo S. Martins, Miguel Carvalho, Hugo Gamboa

Research output: Contribution to journalArticle

20 Citations (Scopus)
63 Downloads (Pure)

Abstract

When a comparison between time series is required, measurement functions provide meaningful scores to characterize similarity between sequences. Quite often, time series appear warped in time, i.e, although they may exhibit amplitude and shape similarity, they appear dephased in time. The most common algorithm to overcome this challenge is the Dynamic Time Warping, which aligns each sequence prior establishing distance measurements. However, Dynamic Time Warping takes only into account amplitude similarity. A distance which characterizes the degree of time warping between two sequences can deliver new insights for applications where the timing factor is essential, such well-defined movements during sports or rehabilitation exercises. We propose a novel measurement called Time Alignment Measurement, which delivers similarity information on the temporal domain. We demonstrate the potential of our approach in measuring performance of time series alignment methodologies and in the characterization of synthetic and real time series data acquired during human movement.

Original languageEnglish
Pages (from-to)268-279
Number of pages12
JournalPattern Recognition
Volume81
DOIs
Publication statusPublished - 1 Sep 2018

Keywords

  • Distance
  • Signal alignment
  • Similarity
  • Time series
  • Time warping

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    Folgado, D., Barandas, M., Matias, R., Martins, R. S., Carvalho, M., & Gamboa, H. (2018). Time Alignment Measurement for Time Series. Pattern Recognition, 81, 268-279. https://doi.org/10.1016/j.patcog.2018.04.003