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

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.