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| Author(s): |
Marcio Valk
Total Authors: 1
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| Document type: | Doctoral Thesis |
| Press: | Campinas, SP. |
| Institution: | Universidade Estadual de Campinas (UNICAMP). Instituto de Matemática, Estatística e Computação Científica |
| Defense date: | 2011-02-22 |
| Examining board members: |
Aluísio de Souza Pinheiro;
Luiz Koodi Hotta;
Francisco Cribari Neto;
Juvêncio Santos Nobre;
Pedro Alberto Morettin
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| Advisor: | Aluísio de Souza Pinheiro |
| Abstract | |
Classifcation and clustering of time series are problems widely explored in the current literature. Many techniques are presented to solve these problems. However, the necessary restrictions in general, make the procedures specific and applicable only to a certain class of time series. Moreover, many of these approaches are empirical. We present methods for classi_cation and clustering of time series based on Quasi U-statistics (Pinheiro et al. (2009) and Pinheiro et al. (2010)). As kernel of U-statistics are used metrics based on tools well known in the literature of time series, including the sample autocorrelation and periodogram. Three main situations are considered: univariate time series, multivariate time series, and time series with outliers. It is demonstrated the asymptotic normality of the proposed tests for a wide class of metrics and models. The methods are also studied by simulation and applied in a real data set. (AU) | |
| FAPESP's process: | 07/02767-6 - Analysis of one- and multidimensional time series via quasi $U$-statistics |
| Grantee: | Marcio Valk |
| Support Opportunities: | Scholarships in Brazil - Doctorate |