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A study of nature-inspired methods for financial trend reversal detection

TitleA study of nature-inspired methods for financial trend reversal detection
Publication TypeArticolo su Rivista peer-reviewed
Year of Publication2010
AuthorsAzzini, A., De Felice Matteo, and Tettamanzi A.G.B.
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6025 LNCS
Pagination161-170
ISBN Number3642122418; 9783642122415
ISSN03029743
KeywordsAnomaly detection, Artificial Immune System, Cellular automata, classification, Complex task, Financial problems, Financial Trend, Negative selection, Particle swarm optimization (PSO), Swarm Intelligence, Turning, Turning points
Abstract

This paper presents an application of two nature-inspired algorithms to the financial problem concerning the detection of turning points. Nature-Inspired methods are receiving a growing interest due to their ability to cope with complex tasks like classification, forecasting and anomaly detection problems. A swarm intelligence algorithm, Particle Swarm Optimization (PSO), and an artificial immune system one, the Negative Selection (NS), are applied to the problem of detection of turning points, modeled as an Anomaly Detection (AD) problem, and their performances are compared. Both methods are found to give interesting results with respect to an unpredictable behavior. © 2010 Springer-Verlag Berlin Heidelberg.

Notes

cited By 1; Conference of EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoMUSART, and EvoTRANSLOG, EvoApplications 2010 ; Conference Date: 7 April 2010 Through 9 April 2010; Conference Code:80274

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-77952335598&doi=10.1007%2f978-3-642-12242-2-17&partnerID=40&md5=3e27ef59f199eed556cad13501997fd6
DOI10.1007/978-3-642-12242-2-17
Citation KeyAzzini2010161