dc.contributor.author | Draganova, C. | en |
dc.contributor.author | Lanitis, A. | en |
dc.contributor.author | Christodoulou, Chris C. | en |
dc.creator | Draganova, C. | en |
dc.creator | Lanitis, A. | en |
dc.creator | Christodoulou, Chris C. | en |
dc.date.accessioned | 2019-11-13T10:39:57Z | |
dc.date.available | 2019-11-13T10:39:57Z | |
dc.date.issued | 2009 | |
dc.identifier.issn | 1865-0929 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/53886 | |
dc.description.abstract | In this study we aim to define a mapping function that relates the general index value among a set of shares to the prices of individual shares. In more general terms this is problem of defining the relationship between multivariate data distributions and a specific source of variation within these distributions where the source of variation in question represents a quantity of interest related to a particular problem domain. In this respect we aim to learn a complex mapping function that can be used for mapping different values of the quantity of interest to typical novel samples of the distribution. In our investigation we compare the performance of standard neural network based methods like Multilayer Perceptrons (MLPs) and Radial Basis Functions (RBFs) as well as Mixture Density Networks (MDNs) and a latent variable method, the General Topographic Mapping (GTM). According to the results, MLPs and RBFs outperform MDNs and the GTM for this one-to-many mapping problem. © 2009 Springer-Verlag. | en |
dc.source | 11th International Conference on Engineering Applications of Neural Networks, EANN 2009 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-78049366928&doi=10.1007%2f978-3-642-03969-0_37&partnerID=40&md5=cc1a66ca417f693cefd9cfde43030828 | |
dc.subject | Radial basis functions | en |
dc.subject | Costs | en |
dc.subject | Mapping | en |
dc.subject | Quantity of interest | en |
dc.subject | IS problems | en |
dc.subject | Image segmentation | en |
dc.subject | Radial basis function networks | en |
dc.subject | Neural Networks | en |
dc.subject | Pattern recognition systems | en |
dc.subject | Multilayer neural networks | en |
dc.subject | Multivariant analysis | en |
dc.subject | Complex mapping | en |
dc.subject | General index | en |
dc.subject | Latent variable methods | en |
dc.subject | Mapping functions | en |
dc.subject | Mixture density | en |
dc.subject | Multi-layer perceptrons | en |
dc.subject | Multivariate data | en |
dc.subject | Multivariate Statistics | en |
dc.subject | Network-based | en |
dc.subject | One-to-Many Mapping | en |
dc.subject | Problem domain | en |
dc.subject | Standard neural | en |
dc.subject | Stock price | en |
dc.subject | Stock Price Prediction | en |
dc.subject | Topographic mapping | en |
dc.title | Isolating stock prices variation with neural networks | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1007/978-3-642-03969-0_37 | |
dc.description.volume | 43 CCIS | en |
dc.description.startingpage | 401 | |
dc.description.endingpage | 408 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Article | en |
dc.description.notes | <p>Sponsors: University of East London | en |
dc.description.notes | London Metropolitan University | en |
dc.description.notes | International Neural Network Society INNS | en |
dc.description.notes | Conference code: 82173</p> | en |
dc.source.abbreviation | Commun. Comput. Info. Sci. | en |
dc.contributor.orcid | Christodoulou, Chris C. [0000-0001-9398-5256] | |
dc.gnosis.orcid | 0000-0001-9398-5256 | |