A new approach of the string theory called the Prediction Model Based on String Invariants (PMBSI) was applied here to time-series forecast. We used 2-end-point open string that satisfies the Dirichlet and Neumann boundary conditions. The initial motivation was to transfer modern physical ideas into the neighboring field called econophysics. The physical statistical viewpoint has proved to be fruitful, namely in the description of systems where many-body effects dominate. However, PMBSI is not limited to financial forecast. The main advantage of PMBSI include absence of the learning phase when large number of parameters must be set. Comparative experimental analysis of PMBSI vs. SVM was performed and the results on artificial and real-world data are presented. PMBSI performance was in a close match with SVM.
Publié le : 2014-02-24
Classification:
Knowledge and Information Engineering,
String theory, time-series forecast, econophysics, PMBSI, SVM,
37M10
@article{cai1041,
author = {Marek Bundzel; Department of Cybernetics and Articial Intelligence, Technical University of Ko\v sice, Letn\'a 9, 040 01 Ko\v sice and Tom\'a\v s Kasanick\'y; Institute of Informatics, Slovak Academy of Sciences, D\'ubravsk\'a cesta 9, 845 07 Bratislava and Richard Pincak; Institute of Experimental Physics, Slovak Academy of Sciences, Ko\v sice},
title = {Experimental Analysis of the Prediction Model Based on String Invariants},
journal = {Computing and Informatics},
volume = {33},
number = {1},
year = {2014},
language = {en},
url = {http://dml.mathdoc.fr/item/cai1041}
}
Marek Bundzel; Department of Cybernetics and Articial Intelligence, Technical University of Košice, Letná 9, 040 01 Košice; Tomáš Kasanický; Institute of Informatics, Slovak Academy of Sciences, Dúbravská cesta 9, 845 07 Bratislava; Richard Pincak; Institute of Experimental Physics, Slovak Academy of Sciences, Košice. Experimental Analysis of the Prediction Model Based on String Invariants. Computing and Informatics, Tome 33 (2014) no. 1, . http://gdmltest.u-ga.fr/item/cai1041/