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