论文标题
Costi：一个新的分类器，用于时间间隔的序列
COSTI: a New Classifier for Sequences of Temporal Intervals
论文作者
论文摘要
时间间隔序列的分类是时间序列分析的一部分，涉及一系列事件。我们提出了一种将问题转换为多元系列分类任务的新方法。我们在新表示形式上使用后者域中的最先进算法之一，以获得比以前领域的最先进方法更好的准确性。我们讨论了此工作流程的局限性，并通过开发一种新的分类方法称为COSTI的新方法（用于时间间隔的时间间隔序列的分类缩写），直接在时间间隔序列上运行。所提出的方法仍然具有高度的准确性，并获得了更好的性能，同时避免了与转换数据相关的缺点。我们提出了时间间隔分类问题的广义版本，其中每个事件都添加了有关其强度的信息。我们还提供两个新的数据集，其中此信息具有很大的价值。
Classification of sequences of temporal intervals is a part of time series analysis which concerns series of events. We propose a new method of transforming the problem to a task of multivariate series classification. We use one of the state-of-the-art algorithms from the latter domain on the new representation to obtain significantly better accuracy than the state-of-the-art methods from the former field. We discuss limitations of this workflow and address them by developing a novel method for classification termed COSTI (short for Classification of Sequences of Temporal Intervals) operating directly on sequences of temporal intervals. The proposed method remains at a high level of accuracy and obtains better performance while avoiding shortcomings connected to operating on transformed data. We propose a generalized version of the problem of classification of temporal intervals, where each event is supplemented with information about its intensity. We also provide two new data sets where this information is of substantial value.
