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Doctoral Thesis
DOI
Document
Author
Full name
Hiron Pereira Farias
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
Piracicaba, 2019
Supervisor
Committee
Piedade, Sonia Maria de Stefano (President)
Alves, Lucilio Rogerio Aparecido
Bacchi, Mirian Rumenos Piedade
Emiliano, Paulo César
Title in Portuguese
Modelagem de séries temporais para fins de previsão
Keywords in Portuguese
Commodity
Modelagem ARMA(p; q)
Série Temporal
Variáveis climáticas
Abstract in Portuguese
Nesse trabalho, exploramos técnicas para análise de séries temporais para fins de previsão. Para tanto, foram considerados dados observados de três séries climáticas e de uma série econômica. Para análise das séries climáticas, foi considerada a modelagem multivariada em comparação com os subsequentes modelos univariados de cada série. Os modelos multivariados e univariados foram comparados com base em seus respectivos resultados preditivos. Para análise da série econômica, considerou-se a modelagem ARMA-GARCH, cuja média condicional e variância condicional são modeladas conjuntamente. Para essa mesma série foi realizada uma modelagem ARIMA em que considerou-se dois casos. No primeiro, a modelagem foi realizada na série original. No segundo, foi realizada na pré-modelagem uma filtragem na série, denominada de sistema de decomposição Wavelet- WavDS, com o objetivo de melhorar o poder preditivo. Na seleção dos modelos ARIMA, considerou-se a metodologia backtesting, em que as previsões são realizadas de forma sequencial, o modelo selecionado foi o que apresentou menor raiz quadrada do erro quadrático médio de previsão (REQM). Toda análise estatística realizada nesse trabalho foi com auxílio do software livre R.
Title in English
Time-series modeling for prediction purposes
Keywords in English
ARMA Modeling (p; q)
Climate variables
Commodity
Time series
Abstract in English
In this study, we explored techniques of time-series analysis for prediction purposes. For that, we considered data observed from three climate series and one economic series. For the analysis of the climate series, we considered the multivariate modelling in comparison with the subsequent univariate models of each series. The multivariate and univariate models were compared based on their respective predictive results. For the analysis of the economic series, the ARMA-GARCH modeling was considered, whose conditional average and conditional variance are modeled together. For this same series, the ARIMA modeling was used, considering two cases. At first, the modeling was performed in the original series. In the second, we carried out a filtering in the series during pre-modeling, called Wavelet- WavDS decomposition system, in order to improve the predictive power. In the selection of ARIMA models, we considered the backtesting methodology in which forecasts are performed in sequence. The model selected showed the lowest square root mean of the prediction square error (REQM). All statistical analyses performed in this work were carried out using the free software R.
 
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Publishing Date
2019-06-05
 
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