Feature Engineering for Improving Financial Derivatives-based Rainfall Prediction
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Cramer:2016:CEC,
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author = "Sam Cramer and Michael Kampouridis and
Alex A. Freitas",
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title = "Feature Engineering for Improving Financial
Derivatives-based Rainfall Prediction",
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booktitle = "Proceedings of 2016 IEEE Congress on Evolutionary
Computation (CEC 2016)",
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year = "2016",
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editor = "Yew-Soon Ong",
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pages = "3483--3490",
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address = "Vancouver",
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month = "24-29 " # jul,
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publisher = "IEEE Press",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-5090-0623-6",
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DOI = "doi:10.1109/CEC.2016.7744231",
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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.",
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notes = "WCCI2016",
- }
Genetic Programming entries for
Sam Cramer
Michael Kampouridis
Alex Alves Freitas
Citations