Predicting and Treating Missing Data with Boot.EXPOS

  • Clara Cordeiro
  • M. Manuela Neves
Conference paper
Part of the Studies in Theoretical and Applied Statistics book series (STAS)


The Boot.EXPOS procedure is an algorithm that combines the use of exponential smoothing methods with the bootstrap methodology for obtaining forecasts. It starts with the selection of an exponential smoothing method and evolves to a bootstrapping design based on the residuals. The time series is reconstructed and forecasts are obtained. That procedure, now extended to “predict” missing values, is named NABoot.EXPOS.


Time Series Unit Root Stationary Time Series Missing Observation Bootstrap Methodology 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Research partially supported by DM/FCT/Ualg and National Funds through FCT—Fundação para a Ciência e a Tecnologia, project PEst-OE/MAT/UI0006/2011, and[4]PTDC/FEDER.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.University of AlgarveFaroPortugal
  2. 2.CEAULLisboaPortugal
  3. 3.ISA, Technical University of LisbonLisboaPortugal

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