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Type: Artigo de periódico
Title: Time-series clustering via quasi U-statistics
Author: Valk, M
Pinheiro, A
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.
Subject: Higher-order asymptotics
non-stationary time series
non-parametric tests
stationary time series
time-series classification
time-series clustering
Country: EUA
Editor: Wiley-blackwell
Citation: Journal Of Time Series Analysis. Wiley-blackwell, v. 33, n. 4, n. 608, n. 619, 2012.
Rights: fechado
Identifier DOI: 10.1111/j.1467-9892.2012.00793.x
Date Issue: 2012
Appears in Collections:Unicamp - Artigos e Outros Documentos

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