论文标题
通过可解释表示
Multivariable times series classification through an interpretable representation
论文作者
论文摘要
多变量时间序列分类是一项任务，由于在各个领域（经济，健康，能源，运输，农作物等）中新问题的扩散，其重要性越来越重要。传统上在单变量环境中使用的方法的直接推断无法经常应用于在多变量问题中获得最佳结果。这主要是由于这些方法无法捕获符合多元时间序列的不同变量之间的关系。迄今为止发布的多元提案提供了竞争成果，但很难解释。在本文中，我们提出了一种时间序列分类方法，该方法通过一组描述性特征来考虑时间序列的替代表示，并考虑到多元时间序列的不同变量之间的关系。我们应用了传统的分类算法，可获得可解释和竞争结果。
Multivariate time series classification is a task with increasing importance due to the proliferation of new problems in various fields (economy, health, energy, transport, crops, etc.) where a large number of information sources are available. Direct extrapolation of methods that traditionally worked in univariate environments cannot frequently be applied to obtain the best results in multivariate problems. This is mainly due to the inability of these methods to capture the relationships between the different variables that conform a multivariate time series. The multivariate proposals published to date offer competitive results but are hard to interpret. In this paper we propose a time series classification method that considers an alternative representation of time series through a set of descriptive features taking into account the relationships between the different variables of a multivariate time series. We have applied traditional classification algorithms obtaining interpretable and competitive results.
