Time Series Classification (TSC) is an extensively researched field from which a broad range of real-world problems can be addressed obtaining excellent results. One sort of the approaches performing well are the so-called dictionary-based techniques. The Temporal Dictionary Ensemble (TDE) is the current state-of-the-art dictionary-based TSC approach. In many TSC problems we find a natural ordering in the labels associated with the time series. This characteristic is referred to as ordinality, and can be exploited to improve the methods performance. The area dealing with ordinal time series is the Time Series Ordinal Classification (TSOC) field, which is yet unexplored. In this work, we present an ordinal adaptation of the TDE algorithm, known as ordinal TDE (O-TDE). For this, a comprehensive comparison using a set of 18 TSOC problems is performed. Experiments conducted show the improvement achieved by the ordinal dictionary-based approach in comparison to four other existing nominal dictionary-based techniques.

翻译：时间序列分类（TSC）是一个被广泛研究且能有效解决多种现实问题的领域，相关方法在此类问题上取得了优异结果。其中表现突出的方法之一是所谓的基于词典的技术。当前最先进的基于词典的TSC方法是时序字典集成（TDE）。在许多TSC问题中，我们发现时间序列对应的标签存在自然顺序，这种特性称为序数性，可用于提升方法性能。处理此类序数时间序列的领域是时间序列序数分类（TSOC），目前尚未被充分探索。本文提出TDE算法的序数自适应版本，即序数TDE（O-TDE）。为此，我们基于18个TSOC问题进行了全面的对比实验。实验结果表明，与四种现有的名义型词典方法相比，基于词典的序数方法在性能上取得了显著提升。