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DTW Barycenter Averaging/Evidence
Method evidence record

DTW Barycenter Averaging

DTW Barycenter Averaging (DBA) is a method for computing the average or representative sequence of a set of time series that respects temporal warping and elastic distance. Unlike Euclidean averaging which requires point-wise alignment, DBA minimizes the sum of Dynamic Time Warping (DTW) distances, producing a meaningful average for sequences with flexible temporal alignments. Introduced by Petitjean and colleagues in 2011, it is widely used in time-series clustering and summarization.

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Source record

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Dynamic Time Warping Barycenter Averaging
Taxonomic method record · process-pipeline / time-series
  • Salvador, S., & Chan, P. (2004). FastDTW: Toward accurate dynamic time warping in linear time and space. Intelligent Data Analysis, 11(5), 561–580. · URL
  • Petitjean, F., Ketterlin, A., & Gançarski, P. (2011). A global averaging method for dynamic time warping, with applications to clustering. Pattern Recognition, 44(3), 678–693. · DOI 10.1016/j.patcog.2010.09.013
  • Cuturi, M., & Blondel, M. (2016). Soft-DTW: A differentiable loss function for time-series. arXiv preprint arXiv:1703.01541. · URL
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Related methods

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Same method familyDiscrete Wavelet Transformmachine-suggested · Relational suggestion, not evidence.See alsoDynamic Time Warpingmachine-suggested · Relational suggestion, not evidence.See alsoHierarchical Clusteringmachine-suggested · Relational suggestion, not evidence.See alsoK-meansmachine-suggested · Relational suggestion, not evidence.

Evidence status

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Sources

3 recorded citations, copied from the method source record.

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