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Wavelet-based clustering for time-series trend detection

2020-11-17 09:41:49
Vincent Talbo, Mehdi Haddab, Derek Aubert, Redha Moulla
arXiv_CV
     
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.

Abstract (translated)

URL

https://arxiv.org/abs/2011.12111

PDF

https://arxiv.org/pdf/2011.12111.pdf

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