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

  • Alexandros Agapitos
  • Michael O’Neill
  • Anthony Brabazon
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7248)

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.

Keywords

Root Mean Square Error Fusarium Head Blight Seasonal Forecast Symbolic Regression Weather Derivative 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alexandros Agapitos
    • 1
  • Michael O’Neill
    • 1
  • Anthony Brabazon
    • 1
  1. 1.Financial Mathematics and Computation Research Cluster Natural Computing Research and Applications Group Complex and Adaptive Systems LaboratoryUniversity College DublinIreland

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