In numerous applications, for instance in predictive maintenance, there is a pression to predict events ahead of time with as much accuracy as possible while not delaying the decision unduly. This translates in the optimization of a trade-off between earliness and accuracy of the decisions, that has been the subject of research for time series of finite length and with a unique label. And this has led to powerful algorithms for Early Classification of Time Series (ECTS). This paper, for the first time, investigates such a trade-off when events of different classes occur in a streaming fashion, with no predefined end. In the Early Classification in Open Time Series problem (ECOTS), the task is to predict events, i.e. their class and time interval, at the moment that optimizes the accuracy vs. earliness trade-off. Interestingly, we find that ECTS algorithms can be sensibly adapted in a principled way to this new problem. We illustrate our methodology by transforming two state-of-the-art ECTS algorithms for the ECOTS scenario. Among the wide variety of applications that this new approach opens up, we develop a predictive maintenance use case that optimizes alarm triggering times, thus demonstrating the power of this new approach.
翻译:在许多应用中,例如预测性维护中, 有一种要求是提前预测事件, 尽可能准确, 而不是不适当地拖延决定。 也就是说, 在决定的耳性和准确性之间实现最佳权衡, 这是时间序列的有限长度和独特标签的研究主题。 这导致了时间序列早期分类( ECTS ) 的强大算法。 本文首次调查了不同类别事件以流传方式发生时的这种权衡, 没有预设的结局。 在开放时间序列的早期分类问题( ECTS ) 中, 任务在于预测事件, 即它们之间的等级和时间间隔, 此时要优化准确度相对于耳朵交换的时间序列。 有意思的是, 我们发现ECTS 算法可以有原则性地适应这一新问题。 我们通过为 ECOTS 设想改变两种最尖端的ECTS 算法来说明我们的方法。 在这种新办法开启的各种应用中, 我们开发了一种预测性维护能力, 从而优化新的提醒时间。