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dc.contributor.authorWong, Charlesen_US
dc.contributor.authorVersace, Massimilianoen_US
dc.date.accessioned2011-11-14T18:17:12Z
dc.date.available2011-11-14T18:17:12Z
dc.date.issued2009-12-15en_US
dc.identifier.urihttp://hdl.handle.net/2144/1976
dc.description.abstractFinancial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.en_US
dc.description.sponsorshipCELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001)en_US
dc.language.isoen_USen_US
dc.publisherBoston University Center for Adaptive Systems and Department of Cognitive and Neural Systemsen_US
dc.relation.ispartofseriesBU CAS/CNS Technical Reports;CAS/CNS-TR-2009-012en_US
dc.rightsCopyright 2009 Boston University. Permission to copy without fee all or part of this material is granted provided that: 1. The copies are not made or distributed for direct commercial advantage; 2. the report title, author, document number, and release date appear, and notice is given that copying is by permission of BOSTON UNIVERSITY TRUSTEES. To copy otherwise, or to republish, requires a fee and / or special permission.en_US
dc.titleModeling Financial Time Series with Artificial Neural Networksen_US
dc.typeTechnical Reporten_US
dc.rights.holderBoston University Trusteesen_US


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