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.

通过组织 shapelets 并使其在不同时间上竞争来构建多样的特征表示，Castor 算法利用 shapelets 对时间序列进行转换，通过广泛的实证研究证明 Castor 提供了比多个最先进的分类器更准确的转换，并且在广泛的消融研究中，我们检验了选择超参数的影响并提供了准确且高效的默认值。