Feature Engineering for Improving Financial Derivatives-based Rainfall Prediction

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Cramer:2016:CEC,
  author =       "Sam Cramer and Michael Kampouridis and 
                 Alex A. Freitas",
  title =        "Feature Engineering for Improving Financial
                 Derivatives-based Rainfall Prediction",
  booktitle =    "Proceedings of 2016 IEEE Congress on Evolutionary
                 Computation (CEC 2016)",
  year =         "2016",
  editor =       "Yew-Soon Ong",
  pages =        "3483--3490",
  address =      "Vancouver",
  month =        "24-29 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-5090-0623-6",
  DOI =          "doi:10.1109/CEC.2016.7744231",
  abstract =     "Rainfall is one of the most challenging variables to
                 predict, as it exhibits very unique characteristics
                 that do not exist in other time series data. Moreover,
                 rainfall is a major component and is essential for
                 applications that surround water resource planning. In
                 particular, this paper is interested in extending
                 previous work carried out on the prediction of rainfall
                 using Genetic Programming (GP) for rainfall
                 derivatives. Currently in the rainfall derivatives
                 literature, the process of predicting rainfall is
                 dominated by statistical models, namely using a
                 Markov-chain extended with rainfall prediction (MCRP).
                 In this paper we further extend our new methodology by
                 looking at the effect of feature engineering on the
                 rainfall prediction process. Feature engineering will
                 allow us to extract additional information from the
                 data variables created. By incorporating feature
                 engineering techniques we look to further tailor our GP
                 to the problem domain and we compare the performance of
                 the previous GP, which previously statistically
                 outperformed MCRP, against our new GP using feature
                 engineering on 21 different data sets of cities across
                 Europe and report the results. The goal is to see
                 whether GP can outperform its predecessor without extra
                 features, which acts as a benchmark. Results indicate
                 that in general GP using extra features significantly
                 outperforms a GP without the use of extra features.",
  notes =        "WCCI2016",
}

Genetic Programming entries for Sam Cramer Michael Kampouridis Alex Alves Freitas

Citations