Hybridizing Extreme Learning Machine and Bio-Inspired Computing Approaches for Improved Stock Market Forecasting

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dc.contributor.authorGocken, Mustafa
dc.contributor.authorBoru, Asli
dc.contributor.authorDosdogru, Ayse Tugba
dc.contributor.authorOzcalici, Mehmet
dc.date.accessioned2025-01-06T17:37:48Z
dc.date.available2025-01-06T17:37:48Z
dc.date.issued2017
dc.description2017 International Artificial Intelligence and Data Processing Symposium (IDAP) -- SEP 16-17, 2017 -- Malatya, TURKEY
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.
dc.description.sponsorshipIEEE Turkey Sect,Anatolian Sci
dc.identifier.isbn978-1-5386-1880-6
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2368
dc.identifier.wosWOS:000426868700176
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherIEEE
dc.relation.ispartof2017 International Artificial Intelligence and Data Processing Symposium (Idap)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectExtreme Learning Machine
dc.subjectGenetic Algorithm
dc.subjectDifferential Evolution
dc.subjectParticle Swarm Optimization
dc.subjectWeighted Superposition Attraction
dc.subjectStock Market Forecasting
dc.titleHybridizing Extreme Learning Machine and Bio-Inspired Computing Approaches for Improved Stock Market Forecasting
dc.typeConference Object

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