Process / pipelineTime-series alignment and averaging
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.
Open in MethodMindSoonVideoSoon
Read the full method
Members only
Sign inSign in with a free account to read this section.
Sources
- Salvador, S., & Chan, P. (2004). FastDTW: Toward accurate dynamic time warping in linear time and space. Intelligent Data Analysis, 11(5), 561–580. DOI: 10.3233/IDA-2004-11508 ↗
- 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. link ↗