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Decomposition Genetic Programming: An Extensive Evaluation on Rainfall Prediction in the Context of Weather Derivatives

Cramer, Sam, Kampouridis, Michael, Freitas, Alex A. (2018) Decomposition Genetic Programming: An Extensive Evaluation on Rainfall Prediction in the Context of Weather Derivatives. Applied Soft Computing, 70 . pp. 208-224. ISSN 1568-4946. (doi:10.1016/j.asoc.2018.05.016) (KAR id:67024)

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Regression problems provide some of the most challenging research opportunities in the area of machine learning,

exhibits unique characteristics of high volatility and chaotic patterns that do not exist in other time series data. Moreover,

extensively evaluates a novel algorithm called Decomposition Genetic Programming (DGP), which is an algorithm

before combining back into the full problem. The GP does this by having a separate regression equation for

when dealing with data sets with high volatility and extreme rainfall values, since these values can be focused on

to the current state-of-the-art (Markov chain extended with rainfall prediction), and six other popular machine

Networks, M5 Rules, M5 Model trees, and k-Nearest Neighbours). Results show that the DGP is able to consistently

DGP has had on the coverage of the rainfall predictions and whether it shows robust performance across different


Item Type: Article
DOI/Identification number: 10.1016/j.asoc.2018.05.016
Uncontrolled keywords: Weather derivatives, rainfall prediction, problem decomposition, genetic programming, genetic algorithm
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Michael Kampouridis
Date Deposited: 15 May 2018 09:23 UTC
Last Modified: 04 Feb 2020 04:10 UTC
Resource URI: (The current URI for this page, for reference purposes)
Kampouridis, Michael:
Freitas, Alex A.:
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