论文标题
时代序列分类的复杂度度量和功能
Complexity Measures and Features for Times Series classification
论文作者
论文摘要
由于世界的渐进性数字化，时间序列分类是不同学科的越来越多的问题。当前，最新的时间序列分类以基于转型的合奏的等级制度票数为主导。该算法由分布在五个大模块中的不同域的几个分类器组成。每个模块根据内部评估过程称量所获得的结果的组合使该算法可以在最新的最新结果中获得最佳结果。一个具有动态时间扭曲的最近的邻居仍然是任何时间序列分类问题的基本分类器，其简单性和良好的结果。尽管表现出色，但它们具有弱点，那就是它们不可解释。在时间序列分类的领域中，准确性和可解释性之间存在权衡。在这项工作中，我们提出了一组能够提取时间序列结构的信息，以面对时间序列分类问题。这些特征的使用允许在时间序列问题中使用传统的分类算法。我们提案的实验结果表明，与最先进的第二和第三最佳模型没有统计学上的显着差异。除了准确性的竞争结果外，我们的建议还可以根据提出的一组特征提供可解释的结果
Classification of time series is a growing problem in different disciplines due to the progressive digitalization of the world. Currently, the state-of-the-art in time series classification is dominated by The Hierarchical Vote Collective of Transformation-based Ensembles. This algorithm is composed of several classifiers of different domains distributed in five large modules. The combination of the results obtained by each module weighed based on an internal evaluation process allows this algorithm to obtain the best results in state-of-the-art. One Nearest Neighbour with Dynamic Time Warping remains the base classifier in any time series classification problem for its simplicity and good results. Despite their performance, they share a weakness, which is that they are not interpretable. In the field of time series classification, there is a tradeoff between accuracy and interpretability. In this work, we propose a set of characteristics capable of extracting information on the structure of the time series to face time series classification problems. The use of these characteristics allows the use of traditional classification algorithms in time series problems. The experimental results of our proposal show no statistically significant differences from the second and third best models of the state-of-the-art. Apart from competitive results in accuracy, our proposal is able to offer interpretable results based on the set of characteristics proposed
