The 21st century has witnessed a growing interest in the analysis of time series data. Whereas most of the literature on the topic deals with real-valued time series, ordinal time series have typically received much less attention. However, the development of specific analytical tools for the latter objects has substantially increased in recent years. The R package otsfeatures attempts to provide a set of simple functions for analyzing ordinal time series. In particular, several commands allowing the extraction of well-known statistical features and the execution of inferential tasks are available for the user. The output of several functions can be employed to perform traditional machine learning tasks including clustering, classification or outlier detection. otsfeatures also incorporates two datasets of financial time series which were used in the literature for clustering purposes, as well as three interesting synthetic databases. The main properties of the package are described and its use is illustrated through several examples. Researchers from a broad variety of disciplines could benefit from the powerful tools provided by otsfeatures.

翻译：21世纪见证了时间序列数据分析日益增长的兴趣。尽管该领域的大部分文献关注实值时间序列，但有序时间序列通常受到的关注要少得多。然而，近年来针对后者的特定分析工具的开发有了显著增长。R包otsfeatures旨在提供一组用于分析有序时间序列的简单函数。特别是，用户可以使用多个命令来提取众所周知的统计特征并执行推断任务。若干函数的输出可用于执行传统的机器学习任务，包括聚类、分类或异常值检测。otsfeatures还包含了两个用于聚类目的文献中使用的金融时间序列数据集，以及三个有趣的合成数据库。本文描述了该包的主要属性，并通过几个示例说明了其用途。来自广泛学科的研究人员可以从otsfeatures提供的强大工具中受益。