Wavelet-based clustering for time-series trend detection
This is an incremental method for retail analytics to detect sales trends, but it applies existing techniques to a specific dataset.
The paper tackles the problem of clustering time-series by trend using a wavelet-based method combined with k-means, applied to 864 daily sales revenue series from 61 retail shops, with results analyzed for different mother wavelets and coefficient importance via PCA.
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.