Data extrapolation and decision making via method of hurwitz-radon matrices

3Citations
Citations of this article
4Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Computational Collective Intelligence needs suitable methods of data extrapolation and decision making. Proposed method of Hurwitz-Radon Matrices (MHR) can be used in extrapolation and interpolation of curves in the plane. For example quotations from the Stock Exchange, the market prices or rate of a currency form a curve. This paper contains the way of data anticipation and extrapolation via MHR method and decision making: to buy or not, to sell or not. Proposed method is based on a family of Hurwitz-Radon (HR) matrices. The matrices are skew-symmetric and possess columns composed of orthogonal vectors. The operator of Hurwitz-Radon (OHR), built from these matrices, is described. Two-dimensional data are represented by the set of curve points. It is shown how to create the orthogonal and discrete OHR and how to use it in a process of data foreseeing and extrapolation. MHR method is interpolating and extrapolating the curve point by point without using any formula or function. © 2011 Springer-Verlag Berlin Heidelberg.

Cite

CITATION STYLE

APA

Jakóbczak, D. (2011). Data extrapolation and decision making via method of hurwitz-radon matrices. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6922 LNAI, pp. 173–182). https://doi.org/10.1007/978-3-642-23935-9_17

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free