Electronic Journal of Statistics

Time series clustering based on nonparametric multidimensional forecast densities

José A. Vilar and Juan M. Vilar

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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.

Article information

Electron. J. Statist., Volume 7 (2013), 1019-1046.

First available in Project Euclid: 15 April 2013

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62H30: Classification and discrimination; cluster analysis [See also 68T10, 91C20] 62M20: Prediction [See also 60G25]; filtering [See also 60G35, 93E10, 93E11]
Secondary: 62G07: Density estimation 62G09: Resampling methods

Time series clustering multidimensional forecast density bootstrap kernel estimation principal components analysis


Vilar, José A.; Vilar, Juan M. Time series clustering based on nonparametric multidimensional forecast densities. Electron. J. Statist. 7 (2013), 1019--1046. doi:10.1214/13-EJS800. https://projecteuclid.org/euclid.ejs/1366031049

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