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).

提供了一个早期分类的时间序列问题的评价方法，并通过一系列实验评估了九种现有算法的性能，同时还介绍了一个包含大多数现有算法的开源库。