Castor: Competing shapelets for fast and accurate time series
  classification
Isak Samsten Zed Lee
Shapelets are discriminative subsequences, originally embedded in
shapelet-based decision trees but have since been extended to shapelet-based
transformations. We propose Castor, a simple, efficient, and accurate time
series classification algorithm that utilizes shapelets to transform time
series. The transformation organizes shapelets into groups with varying
dilation and allows the shapelets to compete over the time context to construct
a diverse feature representation. By organizing the shapelets into groups, we
enable the transformation to transition between levels of competition,
resulting in methods that more closely resemble distance-based transformations
or dictionary-based transformations. We demonstrate, through an extensive
empirical investigation, that Castor yields transformations that result in
classifiers that are significantly more accurate than several state-of-the-art
classifiers. In an extensive ablation study, we examine the effect of choosing
hyperparameters and suggest accurate and efficient default values.