Analyzing categorical time series with the R package ctsfeatures
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Analyzing categorical time series with the R package ctsfeaturesData
2024-03Cita bibliográfica
Á. López-Oriona y J. A. Vilar, «Analyzing categorical time series with the package ctsfeatures», Journal of Computational Science, vol. 76, p. 102233, mar. 2024, doi: 10.1016/j.jocs.2024.102233.
Resumo
[Absctract]: 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, one dataset of sleep stages, and 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.
Palabras chave
ctsfeatures
R package
Categorical time series
Feature extraction
Association measures
R package
Categorical time series
Feature extraction
Association measures
Versión do editor
Dereitos
Atribución 3.0 España
ISSN
1877-7511
1877-7503
1877-7503