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Using attribute construction to improve the predictability of a GP financial forecasting algorithm

Kampouridis, Michael, Otero, Fernando E.B. (2013) Using attribute construction to improve the predictability of a GP financial forecasting algorithm. In: Conference on Technologies and Applications of Artificial Intelligence (TAAI 2013). . pp. 55-60. IEEE ISBN 978-1-4799-2528-5. E-ISBN 978-1-4799-2529-2. (doi:10.1109/TAAI.2013.24)

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http://dx.doi.org/10.1109/TAAI.2013.24

Abstract

Financial forecasting is an important area in computational finance. EDDIE 8 is an established Genetic Programming financial forecasting algorithm, which has successfully been applied to a number of international datasets. The purpose of this paper is to further increase the algorithm’s predictive performance, by improving its data space representation. In order to achieve this, we use attribute construction to create new (high-level) attributes from the original (low-level) attributes. To examine the effectiveness of the above method, we test the extended EDDIE’s predictive performance across 25 datasets and compare it to the performance of two previous EDDIE algorithms. Results show that the introduction of attribute construction benefits the algorithm, allowing EDDIE to explore the use of new attributes to improve its predictive accuracy.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/TAAI.2013.24
Uncontrolled keywords: prediction algorithms; measurement; forecasting; production; algorithm design and analysis; testing; radio frequency
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Fernando Otero
Date Deposited: 07 Aug 2014 19:27 UTC
Last Modified: 14 Oct 2019 20:28 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/42141 (The current URI for this page, for reference purposes)
Otero, Fernando E.B.: https://orcid.org/0000-0003-2172-297X
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