Inflation and Unemployment Forecasting with Genetic Support Vector Regression
Created by W.Langdon from
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- @Article{Sermpinis:2014:JF,
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author = "Georgios Sermpinis and Charalampos Stasinakis and
Konstantinos Theofilatos and
Andreas Karathanasopoulos",
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title = "Inflation and Unemployment Forecasting with Genetic
Support Vector Regression",
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journal = "Journal of Forecasting",
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volume = "33",
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number = "6",
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year = "2014",
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pages = "471--487",
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keywords = "genetic algorithms, genetic programming, support
vector regression, forecasting, inflation,
unemployment",
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publisher = "John Wiley \& Sons Ltd.",
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ISSN = "1099-131X",
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URL = "http://eprints.gla.ac.uk/94791/",
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URL = "http://dx.doi.org/10.1002/for.2296",
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DOI = "doi:10.1002/for.2296",
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abstract = "In this paper a hybrid genetic algorithm-support
vector regression (GA-SVR) model in economic
forecasting and macroeconomic variable selection is
introduced. The proposed algorithm is applied to the
task of forecasting US inflation and unemployment.
GA-SVR genetically optimises the SVR parameters and
adapts to the optimal feature subset from a feature
space of potential inputs. The feature space includes a
wide pool of macroeconomic variables that might affect
the two series under study. The forecasting performance
of GA-SVR is benchmarked with a random walk model, an
autoregressive moving average model, a moving average
convergence/divergence model, a multi-layer perceptron,
a recurrent neural network and a genetic programming
algorithm. In terms of our results, GA-SVR outperforms
all benchmark models and provides evidence on which
macroeconomic variables can be relevant predictors of
US inflation and unemployment in the specific period
under study.",
- }
Genetic Programming entries for
Georgios Sermpinis
Charalampos Stasinakis
Konstantinos A Theofilatos
Andreas S Karathanasopoulos
Citations