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Ordinal Time Series Analysis with the R Package otsfeatures

Author

Listed:
  • 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)

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 .

Suggested Citation

  • Ángel López-Oriona & José A. Vilar, 2023. "Ordinal Time Series Analysis with the R Package otsfeatures," Mathematics, MDPI, vol. 11(11), pages 1-23, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:11:p:2565-:d:1163157
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    References listed on IDEAS

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    1. Christian H. Weiß & Philip K. Pollett, 2014. "Binomial Autoregressive Processes With Density-Dependent Thinning," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(2), pages 115-132, March.
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