In many situations, the measurements of a studied phenomenon are provided sequentially, and the prediction of its class needs to be made as early as possible so as not to incur too high a time penalty, but not too early and risk paying the cost of misclassification. This problem has been particularly studied in the case of time series, and is known as Early Classification of Time Series (ECTS). Although it has been the subject of a growing body of literature, there is still a lack of a systematic, shared evaluation protocol to compare the relative merits of the various existing methods. This document begins by situating these methods within a principle-based taxonomy. It defines dimensions for organizing their evaluation, and then reports the results of a very extensive set of experiments along these dimensions involving nine state-of-the art ECTS algorithms. In addition, these and other experiments can be carried out using an open-source library in which most of the existing ECTS algorithms have been implemented (see https://github.com/ML-EDM/ml_edm).

翻译：在许多研究场景中，现象测量值以序列形式持续输入，其类别预测需尽可能早地完成，以避免过高的时间代价，但也不宜过早而导致误判风险。该问题在时间序列分析领域受到特别关注，被称为时间序列早期分类（ECTS）。尽管相关文献持续增长，但目前仍缺乏系统化、可共享的评估框架来比较现有各类方法的相对优劣。本文首先基于原理构建了这些方法的分类体系，定义了组织评估的维度，随后沿这些维度报告了涵盖九种前沿ECTS算法的超大规模实验成果。此外，相关实验可通过开源库复现与扩展，该库已实现大多数现有ECTS算法（详见 https://github.com/ML-EDM/ml_edm）。