We show that it is possible to achieve the same accuracy, on average, as the most accurate existing interval methods for time series classification on a standard set of benchmark datasets using a single type of feature (quantiles), fixed intervals, and an 'off the shelf' classifier. This distillation of interval-based approaches represents a fast and accurate method for time series classification, achieving state-of-the-art accuracy on the expanded set of 142 datasets in the UCR archive with a total compute time (training and inference) of less than 15 minutes using a single CPU core.

翻译：我们证明了在标准基准数据集集合上，仅使用单一特征类型（分位数）、固定区间和“现成”分类器，平均而言可以达到与现有最精确的区间方法相同的时间序列分类准确率。这种对基于区间方法的精简提炼，形成了一种快速且准确的时间序列分类方法，在UCR档案库扩展的142个数据集上达到了最先进的准确率，且使用单CPU核心的总计算时间（训练与推理）不超过15分钟。