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[en] A SPECTRAL SEQUENTIAL APPROACH TO STUDY NON-STATIONARY TIME SERIE / [pt] UMA ABORDAGEM SEQÜENCIAL ESPECTRAL NO ESTUDO DE SÉRIES TEMPORAIS NÃO ESTACIONÁRIAS

[pt] Diferentes procedimentos têm sido propostos para a
modelagem e previsão de séries temporais sendo que nos
anos recentes muitos dos métodos mais importantes têm sido
formulados na representação espaço de estado. A principal
vantagem de tal abordagem é que se pode usar o Filtro de
Kalman diretamente para, seqüencialmente, atualizar o
vetor de estado.
Apresentamos de forma sistemática a abordagem para a
previsão de Séries Temporais não- Estacionárias formulada
na representação de espaço de estado desenvolvida por
P.Young. A novidade desta abordagem não está na natureza
dos algoritmos recursivos, e sim na maneira como os
hiperparâmetros são obtidos.
Modelling and forecasting of Time Series have been
approached in many different ways. Lately, the most
important approaches have been formulated in a state space
framework. The state space representation enables the
state vector to be sequentially updated in time via the
Kalman filter.
In this dissertation, we present in a systematic way an
approach to modelling and forecasting of non-stationary
time series, formulated in state space terms, and due to
P. Young. The novelty of this methodology is neither the
nature fo the time series models nor the recursive
algorithms, but on how the hyperparameters are estimated / [en] Modelling and forecasting of times Series have been
approached in many different ways. Lately, the most
important approaches have been formulated in a space
framework. The state space representation enables the
state vector to be sequencially updated in time via the
Kalman filter.
In this dissertation, we present in a systematic way an
approach to modelling and forecasting of non-stationary
time series, formulated in state space terms, and due to
P. Young. The novelty of this methodology is neither the
nature of the time series models nor the recursive
algorithms, but on how the hyperparameteres are estimated

Identiferoai:union.ndltd.org:puc-rio.br/oai:MAXWELL.puc-rio.br:8787
Date07 August 2006
CreatorsMAYSA SACRAMENTO DE MAGALHAES
ContributorsREINALDO CASTRO SOUZA
PublisherMAXWELL
Source SetsPUC Rio
LanguagePortuguese
Detected LanguageEnglish
TypeTEXTO

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