Wavelet-based clustering for time-series trend detection

Vincent Talbo; Mehdi Haddab; Derek Aubert; Redha Moulla

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arXiv · 2011.12111

Wavelet-based clustering for time-series trend detection

Vincent Talbo·Mehdi Haddab·Derek Aubert·Redha Moulla

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Abstract

In this paper, we introduce a method performing clustering of time-series on the basis of their trend (increasing, stagnating/decreasing, and seasonal behavior). The clustering is performed using $k$-means method on a selection of coefficients obtained by discrete wavelet transform, reducing drastically the dimensionality. The method is applied on an use case for the clustering of a 864 daily sales revenue time-series for 61 retail shops. The results are presented for different mother wavelets. The importance of each wavelet coefficient and its level is discussed thanks to a principal component analysis along with a reconstruction of the signal from the selected wavelet coefficients.

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BibTeX RIS

Vincent Talbo, Mehdi Haddab, Derek Aubert, Redha Moulla. 2020-11-17. Wavelet-based clustering for time-series trend detection. https://arxiv.org/abs/2011.12111

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