  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.
