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

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@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