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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Official URL https://doi.org/10.1016/j.asoc.2018.05.016 |
Abstract
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
climates.
Item Type: | Article |
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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: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
Depositing User: | Michael Kampouridis |
Date Deposited: | 15 May 2018 09:23 UTC |
Last Modified: | 16 Feb 2021 13:54 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/67024 (The current URI for this page, for reference purposes) |
Kampouridis, Michael: | ![]() |
Freitas, Alex A.: | ![]() |
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