Ordinal Patterns Based Change Points Detection
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866917913924993024 |
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| author | Betken, Annika Micali, Giorgio Schmidt-Hieber, Johannes |
| author_facet | Betken, Annika Micali, Giorgio Schmidt-Hieber, Johannes |
| contents | The ordinal patterns of a fixed number of consecutive values in a time series is the spatial ordering of these values. Counting how often a specific ordinal pattern occurs in a time series provides important insights into the properties of the time series. In this work, we prove the asymptotic normality of the relative frequency of ordinal patterns for time series with linear increments. Moreover, we apply ordinal patterns to detect changes in the distribution of a time series. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03099 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ordinal Patterns Based Change Points Detection Betken, Annika Micali, Giorgio Schmidt-Hieber, Johannes Statistics Theory The ordinal patterns of a fixed number of consecutive values in a time series is the spatial ordering of these values. Counting how often a specific ordinal pattern occurs in a time series provides important insights into the properties of the time series. In this work, we prove the asymptotic normality of the relative frequency of ordinal patterns for time series with linear increments. Moreover, we apply ordinal patterns to detect changes in the distribution of a time series. |
| title | Ordinal Patterns Based Change Points Detection |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2502.03099 |