  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.
