Método bagging para aprimoramento de previsões de séries temporais
Resumo
Different methodologies are proposed and explored aiming to reduce time series forecasting
error. A promising approach consists in combining different forecasts from different models
in order to get a better accuracy, i.e., a smaller forecast error. This work aims to review and
apply the bootstrap aggregating method, also known as bagging, in order to improve time series
forecasting. First, each time series is divided into training and testing time series, and then
the moving block bootstrap methodology is applied to the training series to generate different
resampled time series, and then forecasting for each one of the series is performed and combined,
thus obtaining the final combined forecast. The test data set is used to calculate the accuracy of
the models, individual and combined. A simulation study of time series and application to a real
time series data sets are presented. The chosen and fitted model for each of the time series was
an autoregressive integrated moving average (ARIMA). The accuracy measurements used were
the mean square error and its root, mean arctangent absolute percentage error and the symmetric
mean absolute percentage error. Finally, the impact on the forecasts of the combined model by
varying the resampling method parameters was explored and comparisons between the combined
and individual forecasting methods were also carried out.
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