Clustering has received much attention in Statistics and Machine learning with the aim of developing statistical models and autonomous algorithms which are capable of acquiring information from raw data in order to perform exploratory analysis.Several techniques have been developed to cluster sampled univariate vectors only considering the average value over the whole period and as such they have not been able to explore fully the underlying distribution as well as other features of the data, especially in presence of structured time series. We propose a model-based clustering technique that is based on quantile regression permitting us to cluster bivariate time series at different quantile levels. We model the within cluster density using asymmetric Laplace distribution allowing us to take into account asymmetry in the distribution of the data. We evaluate the performance of the proposed technique through a simulation study. The method is then applied to cluster time series observed from Glob-colour satellite data related to trophic status indices with aim of evaluating their temporal dynamics in order to identify homogeneous areas, in terms of trophic status, in the Gulf of Gabes.
翻译:在统计和机器学习中,集群工作受到极大关注,目的是开发统计模型和自主算法,这些模型和算法能够从原始数据中获得信息,以便进行探索性分析。 已经开发了数种技术,只考虑到整个整个期间的平均价值,才对非象牙矢量进行分组抽样,因此,这些技术无法充分探索数据的基本分布及其他特征,特别是在有结构化的时间序列的情况下。我们提议了基于模型的集群技术,这种技术以孔径回归为基础,使我们能够在不同孔径层次上分组双轨时间序列。我们用非对称拉比分布在集群密度内进行模型,以便我们考虑到数据分布中的不对称性。我们通过模拟研究评估了拟议技术的性能。然后将这种方法应用于从Glob-cour卫星数据观测的有关营养状况指数的集群时间序列,目的是评估其时间动态,以便从营养状况的角度确定加贝斯湾的同质地区。