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|Type:||Artigo de periódico|
|Title:||Time-series clustering via quasi U-statistics|
|Abstract:||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.|
non-stationary time series
stationary time series
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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