论文标题
数据挖掘和时间序列分割通过极端：初步研究
Data mining and time series segmentation via extrema: preliminary investigations
论文作者
论文摘要
时间序列细分是众多数据挖掘工具之一。本文用法语，将当地的极值视为感知上有趣的观点（PIP）。由于卡地亚和佩林和代数估计技术，通过添加剂分解定理对这些时间序列的快速波动的模糊进行了处理，这些定理已经在自动控制和信号处理中有用。我们的方法通过多个计算机插图验证。他们强调了选择阈值对极端检测的重要性。
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.
