Bagging Exponential Smoothing Methods using STL Decomposition and Box-Cox Transformation
AbstractExponential 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 11/14.
Date of creation: 2014
Date of revision:
Contact details of provider:
Postal: PO Box 11E, Monash University, Victoria 3800, Australia
Web page: http://www.buseco.monash.edu.au/depts/ebs/
More information through EDIRC
This paper has been announced in the following NEP Reports:
- NEP-ALL-2014-04-11 (All new papers)
- NEP-ECM-2014-04-11 (Econometrics)
- NEP-FOR-2014-04-11 (Forecasting)
- NEP-GER-2014-04-11 (German Papers)
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Simone Grose).
If references are entirely missing, you can add them using this form.