  Time series data are ubiquitous nowadays. Whereas most of the literature on
the topic deals with real-valued time series, categorical time series have
received much less attention. However, the development of data mining
techniques for this kind of data has substantially increased in recent years.
The R package ctsfeatures offers users a set of useful tools for analyzing
categorical time series. In particular, several functions allowing the
extraction of well-known statistical features and the construction of
illustrative graphs describing underlying temporal patterns are provided in the
package. The output of some functions can be employed to perform traditional
machine learning tasks including clustering, classification and outlier
detection. The package also includes two datasets of biological sequences
introduced in the literature for clustering purposes, as well as three
interesting synthetic databases. In this work, the main characteristics of the
package are described and its use is illustrated through various examples.
Practitioners from a wide variety of fields could benefit from the valuable
tools provided by ctsfeatures.
