Inflation and Unemployment Forecasting with Genetic Support Vector Regression

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@Article{Sermpinis:2014:JF,
  author =       "Georgios Sermpinis and Charalampos Stasinakis and 
                 Konstantinos Theofilatos and 
                 Andreas Karathanasopoulos",
  title =        "Inflation and Unemployment Forecasting with Genetic
                 Support Vector Regression",
  journal =      "Journal of Forecasting",
  volume =       "33",
  number =       "6",
  year =         "2014",
  pages =        "471--487",
  keywords =     "genetic algorithms, genetic programming, support
                 vector regression, forecasting, inflation,
                 unemployment",
  publisher =    "John Wiley \& Sons Ltd.",
  ISSN =         "1099-131X",
  URL =          "http://eprints.gla.ac.uk/94791/",
  URL =          "http://dx.doi.org/10.1002/for.2296",
  DOI =          "doi:10.1002/for.2296",
  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