An increasing number of applications require to recognize the class of an incoming time series as quickly as possible without unduly compromising the accuracy of the prediction. In this paper, we put forward a new optimization criterion which takes into account both the cost of misclassification and the cost of delaying the decision. Based on this optimization criterion, we derived a family of non-myopic algorithms which try to anticipate the expected future gain in information in balance with the cost of waiting. In one class of algorithms, unsupervised-based, the expectations use the clustering of time series, while in a second class, supervised-based, time series are grouped according to the confidence level of the classifier used to label them. Extensive experiments carried out on real datasets using a large range of delay cost functions show that the presented algorithms are able to solve the earliness vs. accuracy trade-off, with the supervised partition based approaches faring better than the unsupervised partition based ones. In addition, all these methods perform better in a wide variety of conditions than a state of the art method based on a myopic strategy which is recognized as being very competitive. Furthermore, our experiments show that the non-myopic feature of the proposed approaches explains in large part the obtained performances.

越来越多的应用程序需要尽可能快地识别传入时间序列的类别，而不会过度影响预测的准确性。在本文中，我们提出了一种新的优化标准，它同时考虑了误分类的成本和延迟决策的成本。基于这个优化标准，我们推导出了一系列非近视算法，这些算法试图预测预期的未来信息增益与等待成本的平衡。在一类基于无监督的算法中，期望使用时间序列的聚类，而在第二类基于监督的算法中，时间序列根据用于标记它们的分类器的置信度进行分组。使用大量延迟成本函数在真实数据集上进行的大量实验表明，所提出的算法能够解决早期与准确度的权衡，基于监督分区的方法比基于无监督分区的方法表现更好。此外，所有这些方法在各种条件下都比基于近视策略的最先进方法表现更好，近视策略被认为是非常有竞争力的。此外，我们的实验表明，所提出方法的非近视特征在很大程度上解释了获得的性能。所有这些方法在各种条件下都比基于近视策略的最先进方法表现更好，近视策略被认为是非常有竞争力的。此外，我们的实验表明，所提出方法的非近视特征在很大程度上解释了获得的性能。所有这些方法在各种条件下都比基于近视策略的最先进方法表现更好，近视策略被认为是非常有竞争力的。此外，我们的实验表明，所提出方法的非近视特征在很大程度上解释了获得的性能。