Multi-Series Heuristics for Exponential Smoothing

R. D. Snyder, C. Shah and C. Lehmer

No 266889, Department of Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: In this paper several heuristics are proposed for calculating the smoothing parameter in exponential smoothing when forecasts of many 'closely' related series are required on a regular basis. The methods are evaluated using both synthetic and real data. They not only compare favourably against several other known forecasting techniques but they are also simple and computationally efficient.

Keywords: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Pages: 23
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ageconsearch.umn.edu/record/266889/files/monash-079.pdf (application/pdf)
https://ageconsearch.umn.edu/record/266889/files/monash-079.pdf?subformat=pdfa (application/pdf)

Related works:
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:ags:monebs:266889

DOI: 10.22004/ag.econ.266889

Access Statistics for this paper

More papers in Department of Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics Contact information at EDIRC.
Bibliographic data for series maintained by AgEcon Search ().