Hybridizing Extreme Learning Machine and bio-inspired computing approaches for improved stock market forecasting

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dc.contributor.authorGöçken, Mustafa
dc.contributor.authorBoru, Asli
dc.contributor.authorDosdo?ru, Ayşe Tu?ba
dc.contributor.authorÖzçalici, Mehmet
dc.date.accessioned2025-01-06T17:29:43Z
dc.date.available2025-01-06T17:29:43Z
dc.date.issued2017
dc.description2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017 -- 16 September 2017 through 17 September 2017 -- Malatya -- 115012
dc.description.abstractUnder today's economic conditions, developing more robust and realistic forecasting methods is needed to make investments more profitable and secure. However, understanding the structure of the stock markets is very difficult because of the dynamic and non-stationary data. In this context, bio-inspired computing approaches including evolutionary computation and swarm intelligence can be used to make more accurate calculations and forecasting results. This paper improved Extreme Learning Machine (ELM) using Genetic Algorithm (GA), Differential Evolution (DE) as a two evolutionary computation methods, and Particle Swarm Optimization (PSO) and Weighted Superposition Attraction (WSA) as a two swarm intelligence methods for stock market forecasting in Turkey. The results of this study show that proposed methods can be successfully used in any real-time stock market forecasting because of the noteworthy improvement in forecasting accuracy. © 2017 IEEE.
dc.identifier.doi10.1109/IDAP.2017.8090336
dc.identifier.isbn978-153861880-6
dc.identifier.scopus2-s2.0-85039919451
dc.identifier.urihttps://doi.org/10.1109/IDAP.2017.8090336
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1305
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofIDAP 2017 - International Artificial Intelligence and Data Processing Symposium
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectDifferential evolution
dc.subjectExtreme learning machine
dc.subjectGenetic algorithm
dc.subjectParticle swarm optimization
dc.subjectStock market forecasting.1
dc.subjectWeighted superposition attraction
dc.titleHybridizing Extreme Learning Machine and bio-inspired computing approaches for improved stock market forecasting
dc.typeConference Object

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