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Warped-Linear Models for Time Series Classification
[article]
2017
arXiv
pre-print
This article proposes and studies warped-linear models for time series classification. The proposed models are time-warp invariant analogues of linear models. Their construction is in line with time series averaging and extensions of k-means and learning vector quantization to dynamic time warping (DTW) spaces. The main theoretical result is that warped-linear models correspond to polyhedral classifiers in Euclidean spaces. This result simplifies the analysis of time-warp invariant models by
arXiv:1711.09156v1
fatcat:pb722xhg4feoto3gmkbimztmpa