论文标题
用于时间序列趋势检测的基于小波的聚类
Wavelet-based clustering for time-series trend detection
论文作者
论文摘要
在本文中，我们介绍了一种根据其趋势（增加，停滞/减少和季节性行为）进行时间序列进行聚类的方法。群集使用$ k $ -Means方法对通过离散小波变换获得的系数选择进行选择，从而大大降低了维度。该方法适用于用例，以用于61家零售店的864个每日销售收入时间序列。结果是针对不同母小波的。由于主成分分析以及从所选小波系数重建信号，讨论了每个小波系数及其水平的重要性。
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.
