Macro-economic Time Series Modeling and Interaction Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Gabriel Kronberger and Stefan Fink and 
                 Michael Kommenda and Michael Affenzeller",
  title =        "Macro-economic Time Series Modeling and Interaction
  booktitle =    "Applications of Evolutionary Computing,
                 EvoApplications 2011: {EvoCOMNET}, {EvoFIN}, {EvoHOT},
                 {EvoMUSART}, {EvoSTIM}, {EvoTRANSLOG}",
  year =         "2011",
  month =        "27-29 " # apr,
  editor =       "Cecilia {Di Chio} and Anthony Brabazon and 
                 Gianni {Di Caro} and Rolf Drechsler and Marc Ebner and 
                 Muddassar Farooq and Joern Grahl and Gary Greenfield and 
                 Christian Prins and Juan Romero and 
                 Giovanni Squillero and Ernesto Tarantino and Andrea G. B. Tettamanzi and 
                 Neil Urquhart and A. Sima Uyar",
  series =       "LNCS",
  volume =       "6625",
  publisher =    "Springer Verlag",
  address =      "Turin, Italy",
  publisher_address = "Berlin",
  pages =        "101--110",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming, Finance,
  isbn13 =       "978-3-642-20519-4",
  DOI =          "doi:10.1007/978-3-642-20520-0_11",
  abstract =     "Macro-economic models describe the dynamics of
                 economic quantities. The estimations and forecasts
                 produced by such models play a substantial role for
                 financial and political decisions. In this contribution
                 we describe an approach based on genetic programming
                 and symbolic regression to identify variable
                 interactions in large datasets. In the proposed
                 approach multiple symbolic regression runs are executed
                 for each variable of the dataset to find potentially
                 interesting models. The result is a variable
                 interaction network that describes which variables are
                 most relevant for the approximation of each variable of
                 the dataset. This approach is applied to a
                 macro-economic dataset with monthly observations of
                 important economic indicators in order to identify
                 potentially interesting dependencies of these
                 indicators. The resulting interaction network of
                 macro-economic indicators is briefly discussed and two
                 of the identified models are presented in detail. The
                 two models approximate the help wanted index and the
                 CPI inflation in the US.",
  notes =        "Part of \cite{DiChio:2011:evo_b} EvoApplications2011
                 held inconjunction with EuroGP'2011, EvoCOP2011 and

Genetic Programming entries for Gabriel Kronberger Stefan Fink Michael Kommenda Michael Affenzeller