Time Alignment Measurement for Time Series. (September 2018)

• Record Type: Journal Article
• Authors: Folgado, Duarte; Barandas, Marília; Matias, Ricardo; Martins, Rodrigo; Carvalho, Miguel; Gamboa, Hugo
• Is Part Of: Pattern recognition. Volume 81(2018:Sep.)
• Page Start: 268
• Page End: 279
• Publication Date: 2018-09
• Subjects: Time series -- Time warping -- Similarity -- Distance -- Signal alignment; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.04.003
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A novel time series measurement to express similarity in temporal domain is proposed. The novel measurement quantifies the degree of temporal distortion between two time series. Our method shows meaningful results on datasets applied to Human motion. 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.