| Autor: |
Truong, Charles, Oudre, Laurent, Vayatis, Nicolas |
| Rok vydání: |
2018 |
| Předmět: |
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| Zdroj: |
Signal Processing, 167:107299, 2020 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1016/j.sigpro.2019.107299 |
| Popis: |
This article presents a selective survey of algorithms for the offline detection of multiple change points in multivariate time series. A general yet structuring methodological strategy is adopted to organize this vast body of work. More precisely, detection algorithms considered in this review are characterized by three elements: a cost function, a search method and a constraint on the number of changes. Each of those elements is described, reviewed and discussed separately. Implementations of the main algorithms described in this article are provided within a Python package called ruptures. |
| Databáze: |
arXiv |
| Externí odkaz: |
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