Analyzing categorical time series with the R package ctsfeatures

TL;DR

The paper introduces the R package ctsfeatures, which provides tools for analyzing categorical time series, including feature extraction, visualization, and applications in machine learning tasks like clustering and classification.

Contribution

It presents the ctsfeatures package, offering new functionalities for analyzing categorical time series, including feature extraction, visualization, and datasets for clustering.

Findings

Provides functions for statistical feature extraction from categorical time series.

Includes datasets for clustering and analysis.

Demonstrates practical applications through examples.

Abstract

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.