文章基本信息

标题：Time series clustering based on nonparametric multidimensional forecast densities
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作者：José A. Vilar ; Juan M. Vilar
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2013
卷号：7
页码：1019-1046
DOI：10.1214/13-EJS800
语种：English
出版社：Institute of Mathematical Statistics
摘要：A new time series clustering method based on comparing forecast densities for a sequence of $k>1$ consecutive horizons is proposed. The unknown $k$-dimensional forecast densities can be non-parametrically approximated by using bootstrap procedures that mimic the generating processes without parametric restrictions. However, the difficulty of constructing accurate kernel estimators of multivariate densities is well known. To circumvent the high dimensionality problem, the bootstrap prediction vectors are projected onto a lower-dimensional space using principal components analysis, and then the densities are estimated in this new space. Proper distances between pairs of estimated densities are computed and used to generate an initial dissimilarity matrix, and hence a standard hierarchical clustering is performed. The clustering procedure is examined via simulation and is applied to a real dataset involving electricity prices series.