In this work we use a novel methodology for the classification of time series data, through a natural, unsupervised data learning process. This strategy is based on the sequential use of Multiple Factor Analysis and an ascending Hierarchical Classification Analysis. These two exploratory techniques complement each other and allow for a clustering of the series based on their time paths and on the reduction of the original dimensionality of the data. The extensive set of graphic and numerical tools available for both methods leads to an exhaustive and rigorous visual and metric analysis of the different trajectories, including their differences and similarities, which will turn out to be responsible of the classes ultimately obtained. An application from Finance, used previously in the literature, highlights the versatility and suitability of this approach.

中文翻译：

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时间序列数据的分类。一种基于降维的无监督方法

在这项工作中，我们通过自然的、无监督的数据学习过程，使用一种新颖的方法对时间序列数据进行分类。该策略基于多因素分析和升序分层分类分析的连续使用。这两种探索性技术相辅相成，允许根据时间路径和数据原始维度的减少对系列进行聚类。可用于这两种方法的大量图形和数值工具导致对不同轨迹的详尽而严格的视觉和度量分析，包括它们的差异和相似之处，这将成为最终获得的类的原因。以前在文献中使用的金融应用程序，