Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation

Exponential smoothing is one of the most popular forecasting methods. We present a technique for the bootstrap aggregation (bagging) of exponential smoothing methods, which results in significant improvements in the forecasts. The bagging uses a Box–Cox transformation followed by an STL decompositio...

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Bibliographic Details
Published inInternational journal of forecasting Vol. 32; no. 2; pp. 303 - 312
Main Authors Bergmeir, Christoph, Hyndman, Rob J., Benítez, José M.
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.04.2016
Elsevier Sequoia S.A
Subjects
Bagging
Bootstrap method
Bootstrapping
Exponential smoothing
Forecasting techniques
Mathematical models
STL decomposition
Studies
Time series
Bootstrapping
Exponential smoothing
STL decomposition
Bagging
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Summary:Exponential smoothing is one of the most popular forecasting methods. We present a technique for the bootstrap aggregation (bagging) of exponential smoothing methods, which results in significant improvements in the forecasts. The bagging uses a Box–Cox transformation followed by an STL decomposition to separate the time series into the trend, seasonal part, and remainder. The remainder is then bootstrapped using a moving block bootstrap, and a new series is assembled using this bootstrapped remainder. An ensemble of exponential smoothing models is then estimated on the bootstrapped series, and the resulting point forecasts are combined. We evaluate this new method on the M3 data set, and show that it outperforms the original exponential smoothing models consistently. On the monthly data, we achieve better results than any of the original M3 participants.
ISSN:0169-2070
1872-8200
DOI:10.1016/j.ijforecast.2015.07.002