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

Abstract

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.