Ordinal Time Series Analysis with the R Package <i>otsfeatures</i>
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 o...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-06-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/11/2565 |
| _version_ | 1797597108788789248 |
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| author | Ángel López-Oriona José A. Vilar |
| author_facet | Ángel López-Oriona José A. Vilar |
| author_sort | Ángel López-Oriona |
| collection | DOAJ |
| description | 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 <b>otsfeatures</b> 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. <b>otsfeatures</b> 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 <b>otsfeatures</b>. |
| first_indexed | 2024-03-11T03:02:08Z |
| format | Article |
| id | doaj.art-e992a088186240fda701908721818062 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T03:02:08Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-e992a088186240fda7019087218180622023-11-18T08:13:46ZengMDPI AGMathematics2227-73902023-06-011111256510.3390/math11112565Ordinal Time Series Analysis with the R Package <i>otsfeatures</i>Ángel López-Oriona0José A. Vilar1Research Group MODES, Research Center for Information and Communication Technologies (CITIC), University of A Coruña, 15071 A Coruña, SpainResearch Group MODES, Research Center for Information and Communication Technologies (CITIC), University of A Coruña, 15071 A Coruña, SpainThe 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 <b>otsfeatures</b> 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. <b>otsfeatures</b> 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 <b>otsfeatures</b>.https://www.mdpi.com/2227-7390/11/11/2565<b>otsfeatures</b>ordinal time seriesfeature extractioncumulative probabilitiesR package |
| spellingShingle | Ángel López-Oriona José A. Vilar Ordinal Time Series Analysis with the R Package <i>otsfeatures</i> Mathematics <b>otsfeatures</b> ordinal time series feature extraction cumulative probabilities R package |
| title | Ordinal Time Series Analysis with the R Package <i>otsfeatures</i> |
| title_full | Ordinal Time Series Analysis with the R Package <i>otsfeatures</i> |
| title_fullStr | Ordinal Time Series Analysis with the R Package <i>otsfeatures</i> |
| title_full_unstemmed | Ordinal Time Series Analysis with the R Package <i>otsfeatures</i> |
| title_short | Ordinal Time Series Analysis with the R Package <i>otsfeatures</i> |
| title_sort | ordinal time series analysis with the r package i otsfeatures i |
| topic | <b>otsfeatures</b> ordinal time series feature extraction cumulative probabilities R package |
| url | https://www.mdpi.com/2227-7390/11/11/2565 |
| work_keys_str_mv | AT angellopezoriona ordinaltimeseriesanalysiswiththerpackageiotsfeaturesi AT joseavilar ordinaltimeseriesanalysiswiththerpackageiotsfeaturesi |