Temperature Forecasting in the Concept of Weather Derivatives: a Comparison between Wavelet Networks and Genetic Programming
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- @InProceedings{conf/eann/AlexandirisK13,
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author = "Antonios K. Alexandiris and Michael Kampouridis",
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title = "Temperature Forecasting in the Concept of Weather
Derivatives: a Comparison between Wavelet Networks and
Genetic Programming",
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editor = "Lazaros S. Iliadis and Harris Papadopoulos and
Chrisina Jayne",
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booktitle = "Proceedings of 14th International Conference on
Engineering Applications of Neural Networks (EANN
2013), Part {I}",
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year = "2013",
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volume = "383",
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series = "Communications in Computer and Information Science",
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pages = "12--21",
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address = "Halkidiki, Greece",
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month = sep # " 13-16",
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publisher = "Springer",
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keywords = "genetic algorithms, genetic programming, weather
derivatives, wavelet networks, temperature
derivatives",
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isbn13 = "978-3-642-41012-3",
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bibdate = "2014-01-25",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/conf/eann/eann2013-1.html#AlexandirisK13",
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URL = "http://dx.doi.org/10.1007/978-3-642-41013-0",
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URL = "http://dx.doi.org/10.1007/978-3-642-41013-0_2",
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DOI = "doi:10.1007/978-3-642-41013-0_2",
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abstract = "The purpose of this study is to develop a model that
accurately describes the dynamics of the daily average
temperature in the context of weather derivatives
pricing. More precisely we compare two state of the art
algorithms, namely wavelet networks and genetic
programming against the classic linear approaches
widely using in the contexts of temperature derivative
pricing. The accuracy of the valuation process depends
on the accuracy of the temperature forecasts. Our
proposed models were evaluated and compared in-sample
and out-of-sample in various locations. Our findings
suggest that the proposed nonlinear methods
significantly outperform the alternative linear models
and can be used for accurate weather derivative
pricing.",
- }
Genetic Programming entries for
Antonios K Alexandiris
Michael Kampouridis
Citations