Results 11 to 20 of about 60,521 (204)
Exact Mean Computation in Dynamic Time Warping Spaces [PDF]
Dynamic time warping constitutes a major tool for analyzing time series. In particular, computing a mean series of a given sample of series in dynamic time warping spaces (by minimizing the Fréchet function) is a challenging computational problem, so far solved by several heuristic and inexact strategies.
Markus Brill+5 more
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Feature trajectory dynamic time warping for clustering of speech segments [PDF]
Dynamic time warping (DTW) can be used to compute the similarity between two sequences of generally differing length. We propose a modification to DTW that performs individual and independent pairwise alignment of feature trajectories.
Lerato Lerato, Thomas Niesler
doaj +4 more sources
The Dynamic Time Warping (DTW) distance is a popular similarity measure for polygonal curves (i.e., sequences of points). It finds many theoretical and practical applications, especially for temporal data, and is known to be a robust, outlier-insensitive alternative to the \frechet distance. For static curves of at most $n$ points, the DTW distance can
Bringmann, Karl+5 more
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Efficient Time Series Clustering by Minimizing Dynamic Time Warping Utilization
Dynamic Time Warping (DTW) is a widely used distance measurement in time series clustering. DTW distance is invariant to time series phase perturbations but has a quadratic complexity.
Borui Cai+4 more
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Improved LDTW Algorithm Based on the Alternating Matrix and the Evolutionary Chain Tree
Dynamic time warping under limited warping path length (LDTW) is a state-of-the-art time series similarity evaluation method. However, it suffers from high space-time complexity, which makes some large-scale series evaluations impossible.
Zheng Zou+3 more
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Voice Transformation Using Two-Level Dynamic Warping and Neural Networks
Voice transformation, for example, from a male speaker to a female speaker, is achieved here using a two-level dynamic warping algorithm in conjunction with an artificial neural network.
Al-Waled Al-Dulaimi+2 more
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Seeded Classification of Satellite Image Time Series with Lower-Bounded Dynamic Time Warping
Satellite Image Time Series (SITS) record the continuous temporal behavior of land cover types and thus provide a new perspective for finer-grained land cover classification compared with the usual spectral and spatial information contained in a static ...
Zheng Zhang+5 more
doaj +1 more source
Learning Discriminative Prototypes with Dynamic Time Warping [PDF]
Dynamic Time Warping (DTW) is widely used for temporal data processing. However, existing methods can neither learn the discriminative prototypes of different classes nor exploit such prototypes for further analysis. We propose Discriminative Prototype DTW (DP-DTW), a novel method to learn class-specific discriminative prototypes for temporal ...
Xiaobin Chang, Frederick Tung, Greg Mori
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Pairwise dynamic time warping for event data [PDF]
We introduce a new version of dynamic time warping for samples of observed event times that are modeled as time-warped intensity processes. Our approach is devel- oped within a framework where for each experimental unit or subject in a sample, one observes a random number of event times or random locations.
Ana Arribas-Gil, Hans-Georg Müller
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Malaysia PM10 Air Quality Time Series Clustering Based on Dynamic Time Warping
Air quality monitoring is important in the management of the environment and pollution. In this study, time series of PM10 from air quality monitoring stations in Malaysia were clustered based on similarity in terms of time series patterns.
Fatin Nur Afiqah Suris+4 more
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