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.
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© 2012 Springer-Verlag Berlin Heidelberg
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Agapitos, A., O’Neill, M., Brabazon, A. (2012). Evolving Seasonal Forecasting Models with Genetic Programming in the Context of Pricing Weather-Derivatives. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2012. Lecture Notes in Computer Science, vol 7248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29178-4_14
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DOI: https://doi.org/10.1007/978-3-642-29178-4_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29177-7
Online ISBN: 978-3-642-29178-4
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