An extensive evaluation of seven machine learning methods for rainfall prediction in weather derivatives

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@Article{Cramer:2017:ESA,
  author =       "Sam Cramer and Michael Kampouridis and 
                 Alex A. Freitas and Antonis K. Alexandridis",
  title =        "An extensive evaluation of seven machine learning
                 methods for rainfall prediction in weather
                 derivatives",
  journal =      "Expert Systems with Applications",
  year =         "2017",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2017.05.029",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0957417417303457",
  abstract =     "Regression problems provide some of the most
                 challenging research opportunities in the area of
                 machine learning, and more broadly intelligent systems,
                 where the predictions of some target variables are
                 critical to a specific application. Rainfall is a prime
                 example, as it exhibits unique characteristics of high
                 volatility and chaotic patterns that do not exist in
                 other time series data. This work's main impact is to
                 show the benefit machine learning algorithms, and more
                 broadly intelligent systems have over the current
                 state-of-the-art techniques for rainfall prediction
                 within rainfall derivatives. We apply and compare the
                 predictive performance of the current state-of-the-art
                 (Markov chain extended with rainfall prediction) and
                 six other popular machine learning algorithms, namely:
                 Genetic Programming, Support Vector Regression, Radial
                 Basis Neural Networks, M5 Rules, M5 Model trees, and
                 k-Nearest Neighbours. To assist in the extensive
                 evaluation, we run tests using the rainfall time series
                 across data sets for 42 cities, with very diverse
                 climatic features. This thorough examination shows that
                 the machine learning methods are able to outperform the
                 current state-of-the-art. Another contribution of this
                 work is to detect correlations between different
                 climates and predictive accuracy. Thus, these results
                 show the positive effect that machine learning-based
                 intelligent systems have for predicting rainfall based
                 on predictive accuracy and with minimal correlations
                 existing across climates.",
  keywords =     "genetic algorithms, genetic programming, Weather
                 derivatives, Rainfall, Machine learning",
}

Genetic Programming entries for Sam Cramer Michael Kampouridis Alex Alves Freitas Antonis K Alexandridis

Citations