id: 06095051
dt: a
an: 06095051
au: Agapitos, Alexandros; O’Neill, Michael; Brabazon, Anthony
ti: Evolving seasonal forecasting models with genetic programming in the
    context of pricing weather-derivatives.
so: Di Chio, Cecilia (ed.) et al., Applications of evolutionary computation.
    EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN, EvoGAMES, EvoHOT,
    EvoIASP, EvoNUM, EvoPAR, EvoRISK, EvoSTIM, and EvoSTOC, Málaga, Spain,
    April 11‒13, 2012. Proceedings. Berlin: Springer (ISBN
    978-3-642-29177-7/pbk). Lecture Notes in Computer Science 7248, 135-144
    (2012).
py: 2012
pu: Berlin: Springer
la: EN
cc: 
ut: 
ci: 
li: doi:10.1007/978-3-642-29178-4_14
ab: Summary: 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.
rv: