| Title | A study of nature-inspired methods for financial trend reversal detection |
|---|---|
| Publication Type | Articolo su Rivista peer-reviewed |
| Year of Publication | 2010 |
| Authors | Azzini, A., De Felice Matteo, and Tettamanzi A.G.B. |
| Journal | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Volume | 6025 LNCS |
| Pagination | 161-170 |
| ISBN Number | 3642122418; 9783642122415 |
| ISSN | 03029743 |
| Keywords | Anomaly 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 |
| URL | https://www.scopus.com/inward/record.uri?eid=2-s2.0-77952335598&doi=10.1007%2f978-3-642-12242-2-17&partnerID=40&md5=3e27ef59f199eed556cad13501997fd6 |
| DOI | 10.1007/978-3-642-12242-2-17 |
| Citation Key | Azzini2010161 |
