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Segmented Dynamic Time Warping: A Comparative and Applicational Study

Segmented Dynamic Time Warping: A Comparative and Applicational Study
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Author(s): Ruizhe Ma (Georgia State University, USA), Azim Ahmadzadeh (Georgia State University, USA), Soukaina Filali Boubrahimi (Georgia State University, USA) and Rafal A. Angryk (Georgia State University, USA)
Copyright: 2019
Pages: 19
Source title: Emerging Technologies and Applications in Data Processing and Management
Source Author(s)/Editor(s): Zongmin Ma (Nanjing University of Aeronautics and Astronautics, China) and Li Yan (Nanjing University of Aeronautics and Astronautics, China)
DOI: 10.4018/978-1-5225-8446-9.ch001

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Abstract

Initially used in speech recognition, the dynamic time warping algorithm (DTW) has regained popularity with the widespread use of time series data. While demonstrating good performance, this elastic measure has two significant drawbacks: high computational costs and the possibility of pathological warping paths. Due to the balance between performance and the tightness of restrictions, the effects of many improvement techniques are either limited in effect or use accuracy as a trade-off. In this chapter, the authors discuss segmented-DTW (segDTW) and its applications. The intuition behind significant features is first established. Then considering the variability of different datasets, the relationship between specific global feature selection parameters, feature numbers, and performance are demonstrated. Other than the improvement in computational speed and scalability, another advantage of segDTW is that while it can be a stand-alone heuristic, it can also be easily combined with other DTW improvement methods.

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