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2013 Time series clustering based on nonparametric multidimensional forecast densities
José A. Vilar, Juan M. Vilar
Electron. J. Statist. 7: 1019-1046 (2013). DOI: 10.1214/13-EJS800


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.


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José A. Vilar. Juan M. Vilar. "Time series clustering based on nonparametric multidimensional forecast densities." Electron. J. Statist. 7 1019 - 1046, 2013.


Published: 2013
First available in Project Euclid: 15 April 2013

zbMATH: 1336.62197
MathSciNet: MR3044508
Digital Object Identifier: 10.1214/13-EJS800

Primary: 62H30 , 62M20
Secondary: 62G07 , 62G09

Keywords: bootstrap , Kernel estimation , multidimensional forecast density , principal components analysis , Time series clustering

Rights: Copyright © 2013 The Institute of Mathematical Statistics and the Bernoulli Society


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