Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation
By Christoph Bergmeir, Rob J Hyndman, C. C. 2016
300 citations recorded by the source corpus.
Subfields: Management Science and Operations Research, Time-Series & Forecasting
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Management Science and Operations Research
- Rob J Hyndman
- Fotios Petropoulos
- Konstantinos Nikolopoulos
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Spyros Makridakis
- Evangelos Spiliotis
- Christoph Bergmeir
In Time-Series & Forecasting
- Fotios Petropoulos
- Rob J Hyndman
- Konstantinos Nikolopoulos
- Spyros Makridakis
- Christoph Bergmeir
- Nikolaos Kourentzes
- Evangelos Spiliotis
- Vassilios Assimakopoulos
Citations in the corpus
- N-BEATS: Neural basis expansion analysis for interpretable time series forecasting (2019, neutral): “BC (Bergmeir et al., 2016) 17.”
- Forecasting Across Time Series Databases using Long Short-Term Memory Networks on Groups of Similar Series (2017, background): “…was able to win the CIF2016 forecasting competition for monthly time series (Štěpnička and Burda, 2016), outperforming state-of-the-art univariate algorithms such as ETS (Hyndman et al., 2008), BaggedETS (Bergmeir et al., 2016), Theta (Hyndman and Billah, 2003), and ARIMA (Box et al., 2015).”
- Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach (2017, background): “This claim is strengthened by the fact that already the BaggedETS submission to the competition outperformed all other methods in this subset, in particular the LSTM methods (Štěpnička and Burda, 2016).”
- Forecasting: theory and practice (2020, methodology): “This led Bergmeir et al. (2016) to propose a bagging version for exponential smoothing, the Bagged ETS. As pre-treatment, after a Box-Cox transformation, the series is decomposed into trend, seasonal, and remainder components via STL decomposition (Cleveland et al., 1990). The resampling stage uses ”
- Exploring the sources of uncertainty: Why does bagging for time series forecasting work? (2018, methodology): “Bergmeir et al. (2016) considered the moving block boot-strap (MBB, K¨unsch, 1989).”
- An empirical survey of data augmentation for time series classification with neural networks (2020, methodology): “[26] exploited STL by decomposing the signal into these components and bootstrapping the remainder using a moving block bootstrap.”