Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 7 (2013), 1019-1046.
Time series clustering based on nonparametric multidimensional forecast densities
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.
Electron. J. Statist., Volume 7 (2013), 1019-1046.
First available in Project Euclid: 15 April 2013
Permanent link to this document
Digital Object Identifier
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
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