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