@article {5382,
	title = {Time-series clustering via quasi U-statistics },
	journal = {Journal of Time Series Analysis },
	volume = {33},
	year = {2012},
	pages = {608-619},
	abstract = {<p>The problem of time-series discrimination and classification is discussed. We propose a novel clustering algorithm based on a class of quasi U-statistics and subgroup decomposition tests. The decomposition may be applied to any concave time-series distance. The resulting test statistics are proven to be asymptotically normal for either i.i.d. or non-identically distributed groups of time-series under mild conditions. We illustrate its empirical performance on a simulation study and a real data analysis. The simulation setup includes stationary vs. stationary and stationary vs. non-stationary cases. The performance of the proposed method is favourably compared with some of the most common clustering measures available.</p>
},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-9892.2012.00793.x},
	author = {Valk, M. and Pinheiro, A.S.}
}
