Data mining and time series segmentation via extrema: preliminary investigations
| Autor: | Fliess, Michel, Join, Cédric |
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| Jazyk: | francouzština |
| Rok vydání: | 2020 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | Time series segmentation is one of the many data mining tools. This paper, in French, takes local extrema as perceptually interesting points (PIPs). The blurring of those PIPs by the quick fluctuations around any time series is treated via an additive decomposition theorem, due to Cartier and Perrin, and algebraic estimation techniques, which are already useful in automatic control and signal processing. Our approach is validated by several computer illustrations. They underline the importance of the choice of a threshold for the extrema detection. Comment: 13th International Conference on Modeling, Optimization and Simulation (MOSIM 2020), Agadir (Morocco), 12-14 November 2020, in French |
| Databáze: | arXiv |
| Externí odkaz: |
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