Ordinal Time Series Analysis with the R Package otsfeatures

Ángel López-Oriona () and José A. Vilar ()
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Ángel López-Oriona: Research Group MODES, Research Center for Information and Communication Technologies (CITIC), University of A Coruña, 15071 A Coruña, Spain
José A. Vilar: Research Group MODES, Research Center for Information and Communication Technologies (CITIC), University of A Coruña, 15071 A Coruña, Spain

Mathematics, 2023, vol. 11, issue 11, 1-23

Abstract: The 21st century has witnessed a growing interest in the analysis of time series data. While 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 .

Keywords: otsfeatures; ordinal time series; feature extraction; cumulative probabilities; R package (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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