Evolving Seasonal Forecasting Models with Genetic Programming in the Context of Pricing Weather-Derivatives

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{agapitos:evoapps12,
  author =       "Alexandros Agapitos and Michael O'Neill and 
                 Anthony Brabazon",
  title =        "Evolving Seasonal Forecasting Models with Genetic
                 Programming in the Context of Pricing
                 Weather-Derivatives",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN,
                 EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK,
                 EvoSTIM, EvoSTOC",
  year =         "2011",
  month =        "11-13 " # apr,
  editor =       "Cecilia {Di Chio} and Alexandros Agapitos and 
                 Stefano Cagnoni and Carlos Cotta and F. {Fernandez de Vega} and 
                 Gianni A. {Di Caro} and Rolf Drechsler and 
                 Aniko Ekart and Anna I Esparcia-Alcazar and Muddassar Farooq and 
                 William B. Langdon and Juan J. Merelo and 
                 Mike Preuss and Hendrik Richter and Sara Silva and 
                 Anabela Simoes and Giovanni Squillero and Ernesto Tarantino and 
                 Andrea G. B. Tettamanzi and Julian Togelius and 
                 Neil Urquhart and A. Sima Uyar and Georgios N. Yannakakis",
  series =       "LNCS",
  volume =       "7248",
  publisher =    "Springer Verlag",
  address =      "Malaga, Spain",
  publisher_address = "Berlin",
  pages =        "135--144",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-29177-7",
  DOI =          "doi:10.1007/978-3-642-29178-4_14",
  size =         "10 pages",
  abstract =     "In this study we evolve seasonal forecasting
                 temperature models, using Genetic Programming (GP), in
                 order to provide an accurate, localised, long-term
                 forecast of a temperature profile as part of the
                 broader process of determining appropriate pricing
                 model for weather-derivatives, financial instruments
                 that allow organisations to protect themselves against
                 the commercial risks posed by weather fluctuations. Two
                 different approaches for time-series modelling are
                 adopted. The first is based on a simple system
                 identification approach whereby the temporal index of
                 the time-series is used as the sole regressor of the
                 evolved model. The second is based on iterated
                 single-step prediction that resembles autoregressive
                 and moving average models in statistical time-series
                 modelling. Empirical results suggest that GP is able to
                 successfully induce seasonal forecasting models, and
                 that autoregressive models compose a more stable unit
                 of evolution in terms of generalisation performance for
                 the three datasets investigated.",
  notes =        "EvoFIN Part of \cite{DiChio:2012:EvoApps}
                 EvoApplications2012 held in conjunction with
                 EuroGP2012, EvoCOP2012, EvoBio'2012 and EvoMusArt2012",
  affiliation =  "Financial Mathematics and Computation Research Cluster
                 Natural Computing Research and Applications Group
                 Complex and Adaptive Systems Laboratory, University
                 College Dublin, Ireland",
}

Genetic Programming entries for Alexandros Agapitos Michael O'Neill Anthony Brabazon

Citations