Results 1 - 10 of 12 for hibon .
Sort by date, Sort by relevance.
1982) and Makridakis and Hibon (2000). Until recently, while the first two approaches often compared their forecasts with various ‘naive’ methods selected from the third group, there was little direct comparison ... While Makridakis and Hibon (2000)
www.economics.ox.ac.uk/materials/papers/5748/paper600.pdf- 253k - 18 Apr 2012 - Cached
The previous competition, M3, consisted of ‘only’ 3003 variables. The best performing method in the M3 competition is the so-called Theta method, see Makridakis and Hibon (2000) and 2.1 ... Theta2, i.e. using θ = 2, had the best sMAPE (see 2.3 below)
www.nuffield.ox.ac.uk/economics/Papers/2019/2019W01_M4_forecasts.pdf- 545k - 9 Jan 2019 - Cached
Hibon, 2000), but argue that is due to confounding parsimony with robustness, as such competitions did not include non-parsimonious but robust models.
www.economics.ox.ac.uk/materials/papers/5081/paper551.pdf- 161k - 25 May 2011 - Cached
Forecast-error correction mechanisms (FErCMs): a classic FErCM is the exponentially-weighted moving- average model, which does well in forecasting competitions (see, e.g., Makridakis and Hibon, 2000) and.
www.economics.ox.ac.uk/materials/papers/14384/paper-779.pdf- 369k - 2 Feb 2016 - Cached
2001) use that analysis to explain the outcomes of forecasting competitions, where the simplicity of a model is viewed as essential for success (see e.g., Makridakis and Hibon, 2000), but
www.nuffield.ox.ac.uk/economics/Papers/2013/UnPredDFHGEM12.pdf- 164k - 22 Feb 2013 - Cached
However, the findings of forecasting competitions (see e.g., Makridakis and Hibon, 2000, Clements and Hendry, 2001, and Fildes and Ord, 2002), extensive appli- cations to forecasting macro time series as ... New York: Springer-Verlag. Makridakis, S., and
www.nuffield.ox.ac.uk/economics/Papers/2004/w14/DFHEqCMRobust.pdf- 163k - 26 Apr 2004 - Cached
We refer to this as ‘optimality theory’ following Makridakis and Hibon (2000). ... Makridakis and Hibon (2000) record the latest in a sequence of such competitions, discussed by Clements and Hendry (2001c).
www.nuffield.ox.ac.uk/economics/Papers/2002/w11/DFHMPCLessons.pdf- 149k - 21 Feb 2002 - Cached
They thereby accounted for the successes and failures of various alternative forecasting approaches, and helped explain the outcomes of forecasting competitions (see e.g., Makridakis and Hibon, 2000, Clements and Hendry, ... Although such results run
www.nuffield.ox.ac.uk/economics/Papers/2004/w15/ForcBasis.pdf- 253k - 11 Jul 2005 - Cached
7See Spyros Makridakis and Michelle Hibon “The M3-competition: Results, conclusions and implications”, International Journal of Forecasting, 16, 451–476, and Robert Fildes and Keith Ord “Forecasting competitions–their role in
www.economics.ox.ac.uk/materials/papers/4864/paper530.pdf- 145k - 8 Feb 2011 - Cached
1. Forecasting Electricity Smart Meter Data Using. Conditional Kernel Density Estimation. Siddharth Arora†and James W. Taylor. Saїd Business School,. University of Oxford, Park End Street, Oxford, OX1 1HP, U.K. OMEGA - The International Journal
people.maths.ox.ac.uk/arora/SmartMeterManuscript_OMEGA.pdf- 890k - 28 Aug 2014 - Cached
Enquiries to Webmaster | Powered by Funnelback Search