2023/04/24 by Ángel López Oriona, Oriona, Ángel López, José Antonio Vilar Fernández +1 · 1 citation
Biochemistry, Genetics and Molecular Biology · Computer Science · Economics, Econometrics and Finance · #Anomaly detection #Artificial intelligence #Categorical variable #Cluster analysis #Complex Systems and Time Series Analysis #Computer science #Data mining #Data science #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine learning #Metabolomics and Mass Spectrometry Studies #Outlier #R package #Series (stratigraphy) #Set (abstract data type) #Time Series Analysis and Forecasting #Time series #Variety (cybernetics)
paper · pdf · doi:10.48550/arxiv.2304.12332
openalex publication_date 2023/04/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.