Data mining and time series segmentation via extrema: preliminary investigations

TL;DR

This paper explores a novel method for time series segmentation using local extrema as perceptually interesting points, employing additive decomposition and algebraic estimation to address fluctuations, validated through computer illustrations.

Contribution

It introduces a new segmentation approach based on extrema and algebraic estimation, emphasizing threshold selection for improved accuracy.

Findings

Threshold choice significantly affects extrema detection.

Additive decomposition helps manage fluctuations around extrema.

Method validated with multiple computer illustrations.

Abstract

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.