Bagging Exponential Smoothing Methods using STL Decomposition and Box-Cox Transformation

Christoph Bergmeir (), Rob Hyndman and Jose M Benitez C22, C53, C63

No 11/14, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: Exponential smoothing is one of the most popular forecasting methods. We present a method for bootstrap aggregation (bagging) of exponential smoothing methods. The bagging uses a Box-Cox transformation followed by an STL decomposition to separate the time series into 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. On the bootstrapped series, an ensemble of exponential smoothing models is estimated. The resulting point forecasts are averaged using the mean. We evaluate this new method on the M3 data set, showing that it consistently outperforms the original exponential smoothing models. On the monthly data, we achieve better results than any of the original M3 participants. We also perform statistical testing to explore significance of the results. Using the MASE, our method is significantly better than all the M3 participants on the monthly data.

Keywords: bagging; bootstrapping; exponential smoothing; STL decomposition. (search for similar items in EconPapers)
Pages: 21
Date: 2014
New Economics Papers: this item is included in nep-ecm, nep-for and nep-ger
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://business.monash.edu/econometrics-and-busine ... ions/ebs/wp11-14.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not Found (http://business.monash.edu/econometrics-and-business-statistics/research/publications/ebs/wp11-14.pdf [301 Moved Permanently]--> https://business.monash.edu/econometrics-and-business-statistics/research/publications/ebs/wp11-14.pdf [301 Moved Permanently]--> https://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp11-14.pdf [301 Moved Permanently]--> https://www.monash.edu/business/econometrics-and-business-statistics-new/research/publications/ebs/wp11-14.pdf)

Related works:
Journal Article: Bagging exponential smoothing methods using STL decomposition and Box–Cox transformation (2016)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:msh:ebswps:2014-11

Ordering information: This working paper can be ordered from
http://business.mona ... -business-statistics

Access Statistics for this paper

More papers in Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics PO Box 11E, Monash University, Victoria 3800, Australia. Contact information at EDIRC.
Bibliographic data for series maintained by Professor Xibin Zhang ().